Technical Interview English Vocabulary: 80 Words That Win FAANG Offers

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TL;DR: 3 Things You Need to Know

• Technical interviews in English are a double challenge: you must demonstrate CS knowledge AND articulate it precisely in a second language. Most non-native speakers lose points on communication, not concepts.
• 80 words across 4 categories cover the vocabulary you'll actually need: algorithms, system design, behavioral interviews, and professional English connectors.
• The STAR method (Situation, Task, Action, Result) is the structural backbone of every behavioral question. Knowing the vocabulary for each component is non-negotiable.

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Why Technical Interview Vocabulary Is the Hidden Differentiator

You can solve the LeetCode problem. You know the optimal time complexity. You have shipped production code at a real company. But when the interviewer says "walk me through your approach," you go blank. Not because you don't know the answer, but because you don't know the English to say it.

This is not a rare situation. It is the norm for non-native English speakers at FAANG and top-tier startup interviews.

In competitive engineering hiring, the technical bar is a filter, not a differentiator. When every candidate can solve a medium LeetCode problem, communication quality is what separates the offer from the rejection. Interviewers at Google, Meta, Amazon, and Microsoft explicitly evaluate how clearly you articulate your thinking: your ability to explain a trade-off, describe a system constraint, or frame a behavioral example with precision.

For Chinese, Japanese, and Korean engineers, among the most technically skilled candidates in the global pool, this communication gap costs disproportionately. Studies of non-native speaker performance in high-stakes oral assessments consistently show that vocabulary gaps, not grammar errors, are the primary source of perceived incompetence. You know what you mean. You just don't have the exact word in the moment.

There are four dimensions to this challenge.

First, technical vocabulary is abstract. Algorithm, complexity, bottleneck, trade-off: these words have no direct equivalent in many East Asian languages. You can't translate them; you have to learn them as concepts mapped to English terms.

Second, behavioral vocabulary is cultural. Words like ownership, ship it, rubber duck debugging, and retrospective are culture-specific. They carry connotations from Silicon Valley engineering culture that don't exist in their literal translations.

Third, precision matters more than fluency. An interviewer does not need you to sound like a native speaker. They need you to be precise. "This solution is O(n log n) which is better than the naive O(n²) approach, but the trade-off is increased space complexity" communicates intelligence. Vague English does the opposite.

Fourth, communication under pressure degrades. Anxiety narrows vocabulary access. The words you half-know are the first to disappear when you're nervous. The solution is not to relax; it's to automate the vocabulary so thoroughly that it's available even under pressure.

This guide gives you 80 words, organized by interview context, with definitions and personalized example sentences. It also gives you the structural frameworks (STAR, system design language, professional connectors) that tell interviewers you think clearly.

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Section 1: 80 Technical Interview Words in 4 Categories

Each entry has three components: the word or phrase, its interview-specific definition, and an personalized example sentence at intermediate level, the kind of sentence you'd actually use in an interview, not a textbook.

Category A: Data Structures & Algorithms Vocabulary

These are the 20 words you'll use in every coding interview. They describe what your solution does, how it performs, and why you made the choices you made.

Word / Phrase	Interview Definition	Example Sentence
algorithm	A step-by-step procedure for solving a problem	"My algorithm processes each node once, so it runs in linear time."
complexity	A measure of how resource usage grows as input size increases	"The complexity of this approach improves from quadratic to linearithmic with a better data structure."
time complexity	How the running time of an algorithm scales with input size	"The time complexity of binary search is O(log n), which makes it efficient for large datasets."
space complexity	How much additional memory an algorithm uses as input grows	"By reusing the input array instead of allocating a new one, I reduced the space complexity to O(1)."
optimization	The process of improving a solution's efficiency or correctness	"The first step in my optimization was to eliminate redundant computations inside the inner loop."
iterate	To repeatedly apply a process, typically with a loop	"I iterate through the array once, keeping track of the maximum value seen so far."
recursion	A technique where a function calls itself to solve a smaller version of the same problem	"Recursion is natural here because the tree structure is self-similar at every level."
trade-off	A situation where improving one thing makes something else worse	"The trade-off between time and space complexity is the central design decision in this problem."
edge case	An input or condition at the extreme boundary of expected values	"I should handle the edge case where the input array is empty before entering the main logic."
bottleneck	The part of a system that limits overall performance	"Profiling revealed that the database query was the bottleneck, not the sorting algorithm."
latency	The delay before a transfer of data begins following an instruction	"Adding a cache reduces latency for repeated requests because the data no longer needs to be recomputed."
throughput	The amount of work completed per unit of time	"To increase throughput, we can process requests in parallel instead of sequentially."
scalability	The ability of a system to handle increased load without degraded performance	"The design lacks scalability because every new user doubles the number of database writes."
refactor	To restructure existing code without changing its external behavior	"I would refactor this into a helper function to make the logic reusable and easier to test."
modular	Designed in independent, interchangeable components	"Keeping the code modular means we can swap out the sorting function later without touching the rest."
abstraction	The principle of hiding implementation details behind a simple interface	"Using a stack as an abstraction here means the calling code doesn't need to know how the data is stored internally."
encapsulation	Bundling data and the functions that operate on it, hiding internal state	"Encapsulation keeps the class's internal state safe from external modification."
debugging	The process of identifying and fixing errors in code	"During debugging, I added logging statements at each step to trace where the value was going wrong."
iteration	One complete pass through a loop or one cycle of a repeated process	"Each iteration of the outer loop processes one row of the matrix."
constraint	A limitation on the input, time, or memory that shapes the valid solution space	"Given the constraint that the array is sorted, we can rule out any O(n²) approach."

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Category B: System Design Vocabulary

System design interviews test whether you can think at scale. These 20 words are the vocabulary of distributed systems, the language interviewers expect when they ask "design a URL shortener" or "how would you build Twitter."

Word / Phrase	Interview Definition	Example Sentence
architecture	The high-level structure of a system and how its components interact	"Before diving into components, I want to describe the overall architecture and then zoom into each layer."
microservices	A design approach where an application is built as a collection of small, independently deployable services	"Migrating from a monolith to microservices allowed each team to deploy their service independently."
monolith	A single, unified application where all components are tightly coupled	"Starting as a monolith makes sense here — we can extract services later once the domain is better understood."
distributed system	A system where components run on multiple computers that communicate over a network	"In a distributed system, we have to assume that any node can fail at any time."
load balancing	Distributing incoming requests across multiple servers to prevent any one server from being overwhelmed	"Load balancing across three server instances ensures no single server becomes a bottleneck."
caching	Storing frequently accessed data in fast memory to reduce retrieval time	"Caching the top 1% of URLs would handle the vast majority of read traffic without hitting the database."
database sharding	Partitioning a database into smaller, faster, more manageable pieces spread across multiple machines	"With 10 billion rows, database sharding by user ID would keep each shard's size manageable."
redundancy	Duplication of components to increase reliability and availability	"Adding a secondary replica provides redundancy — if the primary fails, traffic fails over automatically."
fault tolerance	The ability of a system to continue operating correctly even when some components fail	"Fault tolerance at this layer means replicating the message queue across three availability zones."
consistency	The guarantee that all nodes in a distributed system see the same data at the same time	"There's a fundamental tension between consistency and availability in a distributed database."
availability	The proportion of time a system is operational and accessible	"The design targets 99.9% availability, which allows for less than 9 hours of downtime per year."
partitioning	Dividing data or functionality across multiple nodes or systems	"Horizontal partitioning by region reduces cross-region latency for most users."
API gateway	A server that acts as the single entry point for all client requests, routing them to appropriate services	"The API gateway handles authentication and rate limiting before requests reach any internal service."
rate limiting	Controlling how many requests a client can make in a given time window	"Rate limiting at 1,000 requests per minute per user prevents a single client from overwhelming the system."
message queue	A buffer that stores messages between producers and consumers, decoupling the two	"Using a message queue means the notification service can fall behind without affecting the core transaction flow."
pub/sub	A messaging pattern where publishers send messages to topics and subscribers receive them	"The pub/sub model here means adding a new consumer — say, an analytics service — requires zero changes to the publisher."
CDN	Content Delivery Network — a geographically distributed network of servers that delivers static content closer to users	"Serving static assets through a CDN reduces page load time from 800ms to 90ms for users in Asia."
horizontal scaling	Adding more machines to handle increased load	"Horizontal scaling is preferable here because the workload is stateless and easy to distribute."
vertical scaling	Increasing the capacity of a single machine (more CPU, RAM, storage)	"Vertical scaling has a ceiling — at some point you can't buy a bigger server, and the cost grows non-linearly."
technical debt	The cost of choosing a faster, easier solution now that will require more work to fix later	"The current implementation has significant technical debt — we took shortcuts to hit the launch deadline that we'll need to pay back."

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Category C: Behavioral Interview Vocabulary

Behavioral questions ("Tell me about a time you...") are not soft questions. They are a structured assessment of your judgment, collaboration, and impact. These 20 words are the vocabulary of that assessment.

Word / Phrase	Interview Definition	Example Sentence
initiative	Acting independently without being told to, anticipating what needs to be done	"I took the initiative to write the post-mortem document before the team lead asked, which helped us identify the root cause faster."
accountability	Taking responsibility for your actions and their outcomes, including failures	"When the deployment caused a 30-minute outage, I owned the accountability — I wrote the incident report and presented it to the team."
stakeholder	Anyone with an interest in or affected by the outcome of a project	"Aligning with stakeholders early meant we avoided a three-week rework cycle when requirements changed."
cross-functional	Involving people or teams from different departments or disciplines	"I led a cross-functional effort between engineering, product, and legal to ship the data privacy feature on time."
ownership	A mindset of treating a project or area as your personal responsibility, beyond your formal role	"Ownership in this context meant I stayed through the incident until the root cause was resolved, even though it wasn't technically my service."
retrospective	A structured meeting held after a project or sprint to review what went well and what to improve	"After the launch failure, we ran a retrospective and identified three process gaps we've since addressed."
iterate (agile)	To release a working version quickly, then improve it in repeated cycles based on feedback	"We chose to iterate on a minimal version first rather than spend six months building the complete solution."
ship	To release a feature or product to users	"The goal of that sprint was to ship a working prototype that internal users could test before the end of Q3."
feedback loop	A mechanism for collecting information about outcomes and using it to improve future actions	"We added user analytics to create a tighter feedback loop — we could see within 48 hours whether the change was working."
scope creep	The gradual expansion of a project's requirements beyond its original boundaries	"Scope creep was the primary risk — we had to make a deliberate decision to defer three features to the next release."
align	To bring different parties to a shared understanding or direction	"Before writing a single line of code, I aligned with the product manager on what success looked like."
escalate	To raise a problem to a higher authority or more senior team member when it cannot be resolved at the current level	"When I couldn't get a decision on the API contract, I escalated to my manager, who resolved it with the other team lead in one meeting."
prioritize	To order tasks or goals by their relative importance or urgency	"Given the two-week deadline, I had to prioritize the features by user impact and deprioritize anything that wasn't on the critical path."
impact	The measurable effect of your work on users, the business, or technical outcomes	"The impact of the caching layer was a 60% reduction in database load, which directly lowered our infrastructure costs."
influence	Changing the direction, behavior, or opinion of others, especially without formal authority	"I influenced the team's decision to adopt the new framework by writing a comparative analysis and presenting it at the tech review."
mentor	To guide and develop a more junior team member through advice, feedback, and knowledge sharing	"I mentored two junior engineers during my time there, pair-programming with them weekly and reviewing their design documents."
collaborate	To work jointly with others toward a shared goal, actively contributing and coordinating	"The feature required me to collaborate closely with the mobile team, which meant daily syncs and shared documentation."
deliver	To complete and hand over a working result within the agreed scope, quality, and timeline	"Despite the ambiguous requirements, I delivered a working API within the sprint timeline by making early, explicit assumptions and validating them."
pivot	To make a significant change in approach based on new information or results	"Three weeks in, user testing showed our initial approach was wrong. We had to pivot quickly, which meant cutting two planned features."
drive	To push a project or goal forward through active effort and leadership	"I drove the migration project from start to finish — coordinating across four teams and delivering it two weeks ahead of schedule."

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Category D: General Professional English for Interviews

These are not technical words. They are the connective tissue of interview communication, the phrases that show you think clearly, qualify your statements appropriately, and engage with the interviewer's questions.

Non-native speakers often know the technical vocabulary but lack these professional connectors. Without them, answers sound choppy, overconfident, or evasive. With them, answers sound structured and senior.

Phrase	Function	Example in an Interview
elaborate	Signal that you're about to add more detail	"Could you elaborate on the scale requirements? That would change my design significantly."
clarify	Confirm or resolve ambiguity before proceeding	"Before I start, I want to clarify — are we optimizing for read performance or write performance?"
rephrase	Restate a question or idea to confirm understanding	"Let me rephrase that constraint to make sure I have it right: we have a 100ms SLA for 99th percentile response times?"
walk through	Guide someone step by step through your thinking	"Let me walk through my reasoning before I write any code."
break down	Divide a complex problem into smaller parts	"I'll break down the problem into three components: data modeling, API design, and caching strategy."
in terms of	Specify the dimension you're discussing	"In terms of time complexity, this is optimal. The question is whether the space trade-off is acceptable."
to elaborate	Introduce a more detailed explanation	"To elaborate on the latency concern — the bottleneck is the synchronous call to the third-party API, which we can't control."
to circle back	Return to a point raised earlier	"I want to circle back to the edge case I mentioned — it becomes important once we consider the concurrent write scenario."
approach	The overall method or strategy you're proposing	"My approach would be to start with a simple solution, establish correctness, and then optimize."
considerations	Factors that should influence a decision	"The main considerations here are consistency requirements, expected read/write ratio, and team familiarity with the technology."
trade-offs	The costs and benefits of different options	"There are real trade-offs between these two designs, and the right choice depends on the read/write ratio."
in my experience	Qualify a statement with personal evidence	"In my experience, starting with a monolith and extracting services later is less risky than starting distributed."
I'd argue that	Introduce a position you're prepared to defend	"I'd argue that the additional complexity of microservices isn't justified at this scale."
one way to think about it	Offer a mental model or analogy	"One way to think about this is that the cache is a buffer between the fast and slow parts of the system."
that said	Acknowledge a previous point before introducing a counterpoint	"That said, there are scenarios where the simpler approach would fail — specifically, high-concurrency writes."
to be specific	Signal that you're about to give a concrete example	"To be specific, I reduced query time from 1.2 seconds to 40 milliseconds by adding a composite index."
building on that	Extend a point already made	"Building on that — if we add a read replica, we can handle the read scaling without changing the write path at all."
to your point	Acknowledge and engage with something the interviewer said	"To your point about consistency — you're right that eventual consistency won't work here given the financial nature of the data."
the crux of the matter	Identify the central or most important point	"The crux of the matter is whether we prioritize write throughput or read latency — everything else follows from that decision."
it depends on	Recognize that the answer is context-sensitive	"It depends on the access pattern. If reads far outnumber writes, a cache makes sense. If writes dominate, it adds complexity without benefit."

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Section 2: STAR Method Vocabulary Guide

The STAR method is the single most important framework for behavioral interviews. Every major tech company (Google, Amazon, Meta, Microsoft, Apple) explicitly uses competency-based behavioral assessment, and STAR is the expected response structure.

What STAR means:

• Situation — The context and background. Where were you, what was happening?
• Task — Your specific responsibility. What were you expected to do?
• Action — What you actually did. The concrete steps you took.
• Result — The measurable outcome. What happened because of your actions?

The ratio interviewers want is roughly 10% / 15% / 50% / 25%. Most candidates over-invest in Situation and under-deliver on Action and Result. That's where the vocabulary matters most.

STAR Opening Phrases

Situation openers (set the scene quickly):

1. "This happened during my time at [company], when our team was responsible for..."
2. "The context was a critical migration project with a hard deadline of..."
3. "We were in the middle of a product launch when..."
4. "Shortly after I joined the team, we encountered a situation where..."
5. "During Q3 of last year, our service started experiencing..."

Task openers (state your ownership):

1. "My specific responsibility was to..."
2. "I was accountable for delivering..."
3. "The task fell to me because..."
4. "I owned the end-to-end design and implementation of..."
5. "The team needed someone to drive the..."

Action openers (show your initiative and thinking):

1. "The first thing I did was..."
2. "I started by breaking the problem into three components..."
3. "My approach was to first understand the root cause before proposing any solution..."
4. "I decided to escalate early, because..."
5. "Rather than accepting the existing approach, I proposed an alternative by..."

Result openers (lead with numbers):

1. "The outcome was a [X]% improvement in..."
2. "As a direct result, we reduced [metric] from X to Y..."
3. "The feature shipped on time and within budget, and subsequently..."
4. "We received direct feedback from the engineering director that..."
5. "The impact was significant: specifically, [quantified outcome]..."

Full STAR Example Answer

Question: "Tell me about a time you dealt with a difficult technical problem."

Situation: During my second year at the company, our main product API started experiencing intermittent 500 errors that only appeared under high load — specifically during peak traffic hours. The errors were non-deterministic and difficult to reproduce in staging.

Task: I was assigned as the engineer responsible for identifying the root cause and resolving the issue. The constraint was that we had a major customer demo in eight days, and the instability was unacceptable for that event.

Action: My approach was methodical: I started by analyzing the error logs to establish a pattern before touching any code. I noticed the errors correlated with specific database query patterns, which suggested a bottleneck rather than a code bug. I added granular logging to iterate through the call stack and isolate the problem. To be specific, I found that a particular ORM query was performing a full table scan on a 50-million-row table because a composite index was missing. I wrote a database migration, tested it in a replica environment to verify correctness, and deployed it with a feature flag so we could roll back instantly if anything went wrong. I also circled back to review the other five highest-traffic queries to check for the same issue — I found two more.

Result: The fix reduced the error rate from approximately 0.8% to near zero within 24 hours of deployment. The impact on p99 response time was a reduction from 1.4 seconds to 210 milliseconds. The customer demo proceeded without any issues. I subsequently wrote a documentation guide for the team on identifying and preventing this class of query performance issue, which we've used in three code reviews since.

Note the vocabulary at work: constraint, approach, bottleneck, iterate, to be specific, circle back, impact. These are not decorative. They are signals to the interviewer that you think in structured, professional engineering terms.

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Section 3: System Design Interview Language

System design interviews don't have a single right answer. Interviewers are evaluating whether you think like a senior engineer: can you structure ambiguity, reason about trade-offs, and communicate your decisions clearly?

The vocabulary patterns below are what senior engineering candidates sound like. Study them not to memorize scripts, but to internalize the thinking patterns they represent.

How to Structure Your Answer

The first two minutes of a system design interview determine the trajectory of the rest. Strong candidates use a consistent structure:

1. Clarify requirements first. "Before I start designing, I want to clarify a few things. What's the expected scale? Are we optimizing for reads or writes? What's the acceptable latency for the critical path?"

2. State your approach explicitly. "My approach will be: define the data model, design the API, then talk through the main components and their interactions. I'll flag trade-offs as I go."

3. Drive toward a decision. "Given the constraints you've described, I'd lean toward a SQL database over NoSQL here, because the relational structure of the data is a better fit. That said, if write volume exceeds X, we'd want to revisit that decision."

10 Signal Phrases That Show Senior Thinking

These phrases signal architectural maturity. They appear in the answers of candidates who receive strong hiring recommendations.

Signal Phrase	What It Communicates
"The first question I'd ask is..."	You gather requirements before designing
"The trade-off between X and Y is..."	You reason about competing concerns
"A key consideration here is..."	You prioritize what matters
"I'd start simple and add complexity only when needed..."	You avoid premature optimization
"This assumes [assumption] — if that changes, the design would change significantly..."	You make assumptions explicit
"The bottleneck in this design is likely..."	You anticipate failure points
"One failure mode we need to handle is..."	You think about resilience
"If I were to scale this to 10x traffic..."	You think ahead without losing the current scope
"I would monitor this with..."	You think about operations and observability
"The simplest thing that could work is... and here's when we'd outgrow it..."	You understand the evolution of systems

Common Mistakes by Non-Native Speakers

These are patterns that appear frequently in interviews with non-native English speakers and consistently draw negative feedback from interviewers.

Mistake 1: Jumping to solutions without clarifying.

• What happens: The candidate starts describing a specific technology before the requirements are established.
• What interviewers think: "They don't ask clarifying questions. That's a risk in a real engineering environment."
• Fix: "Before I propose a design, can I ask a few questions to make sure I understand the constraints?"

Mistake 2: Describing instead of deciding.

• What happens: "We could use MySQL, or we could use Cassandra. Both have advantages. MySQL is relational. Cassandra is distributed."
• What interviewers think: "They can recite facts but they can't make a call."
• Fix: "Given the read-heavy workload and structured data, I'd go with PostgreSQL. Here's why..."

Mistake 3: Using vague positive qualifiers.

• What happens: Candidates say things like "this is very efficient" or "this approach is much better."
• What interviewers think: "Better by what measure? How much better?"
• Fix: Be specific. "This reduces time complexity from O(n²) to O(n log n), which matters at 10 million records." Quantify whenever possible.

Mistake 4: Direct translation from L1 sentence structure.

• Chinese example: "This problem, I think can be solved by using..." → "I think this problem can be solved by using..."
• Japanese example: Overuse of "I feel that..." in technical contexts → "My assessment is that..." or "The data suggests..."
• Korean example: Omitting the subject in English sentences where it's required

Mistake 5: Silence instead of thinking-out-loud.

• What happens: The candidate goes quiet while thinking, causing the interviewer to wonder if they're stuck.
• Fix: Narrate your thinking. "Let me think about this for a moment... I'm considering whether the consistency requirement here rules out an eventually consistent store..." This is the difference between seeming stuck and seeming thoughtful.

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Section 4: 30-Day Pre-Interview Vocabulary Sprint

Four weeks before your interview, run this focused vocabulary sprint. The sequence matters: algorithms first, then system design, then behavioral, then integration.

Week 1 — Category A: Algorithms Vocabulary

Goal: Automate the 20 algorithm words so they appear naturally when you talk through code.

Daily practice (20 minutes):

• Day 1–2: Learn the first 10 words. Add them to Rhythm Word and complete the first SRS session.
• Day 3–4: Learn the remaining 10 words. Complete SRS sessions for all 20.
• Day 5: Do a LeetCode Easy or Medium problem entirely out loud. Force yourself to use at least 5 vocabulary words from Category A.
• Day 6: Explain two LeetCode solutions you've solved before to an imaginary interviewer. Record yourself.
• Day 7: Review any Category A words that Rhythm Word's SRS system flagged as weak. Focus there.

Week 1 checkpoint: Can you explain the time and space complexity of a merge sort, a binary search, and a hash table lookup in clear English without hesitating?

Week 2 — Category B: System Design Vocabulary

Goal: Be able to propose, compare, and justify system design choices using precise vocabulary.

Daily practice (25 minutes):

• Day 8–9: Learn the first 10 system design words. Add them to Rhythm Word.
• Day 10–11: Learn the remaining 10. Complete SRS sessions for all 20.
• Day 12: Take one classic system design problem (URL shortener, Twitter timeline, rate limiter) and talk through your design for 15 minutes, forcing yourself to use at least 8 Category B words.
• Day 13: Read one system design blog post or architecture article. Note every Category B word you encounter in context.
• Day 14: Review flagged words in Rhythm Word. Repeat the out-loud design exercise.

Week 2 checkpoint: Can you explain the trade-off between horizontal and vertical scaling, and when you'd choose each, in under 60 seconds?

Week 3 — Category C: Behavioral Vocabulary

Goal: Have STAR answers ready that use the behavioral vocabulary naturally.

Daily practice (25 minutes):

• Day 15–16: Learn the 20 behavioral words. Add to Rhythm Word.
• Day 17: Write out three STAR stories from your own experience. Use at least 3 behavioral words in each story.
• Day 18: Practice the three stories out loud, timing yourself. Each answer should be 2–3 minutes.
• Day 19: Focus on the Result section. Rewrite each story's result paragraph to include a specific number or measurable outcome.
• Day 20: Practice answering the five most common behavioral questions:
  • "Tell me about a time you had a conflict with a teammate."
  • "Describe a situation where you had to deliver under a tight deadline."
  • "Tell me about a time you made a significant technical decision."
  • "Give an example of a time you influenced a team without formal authority."
  • "Tell me about a time you failed."
• Day 21: Review flagged behavioral words in Rhythm Word.

Week 3 checkpoint: Can you tell three distinct STAR stories in under 3 minutes each, with at least one measurable result in each?

Week 4 — Mock Interview Integration

Goal: Combine all vocabulary under realistic interview pressure.

• Day 22–23: Do two mock coding interviews. After each one, review which vocabulary words you missed or avoided, then add them to your Rhythm Word review queue.
• Day 24: Do one mock system design interview (use a service like Pramp, interviewing.io, or a friend). Record it if possible.
• Day 25: Watch the recording back (or ask your practice partner for feedback). Which vocabulary gaps appeared?
• Day 26: Focused Rhythm Word session on every word you flagged as a gap in days 22–25.
• Day 27–28: Two more mock interviews. This time, actively use the Category D connector phrases (elaborate, walk through, circle back, to your point, it depends on). They should feel natural.
• Day 29: Rest. No new vocabulary.
• Day 30: Light review of your weakest 10 words in Rhythm Word. Confidence session only.

Month checkpoint: You should be able to conduct a 45-minute mock technical interview (coding, system design, and behavioral) using the vocabulary from this guide without hesitating or falling back on vague language.

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FAQ

What vocabulary do I need for a technical interview in English?

You need vocabulary across four areas: (1) algorithms and data structures — words like complexity, trade-off, edge case, recursion, bottleneck; (2) system design — architecture, scalability, fault tolerance, caching, consistency; (3) behavioral — ownership, accountability, impact, initiative, stakeholder; and (4) professional connectors — phrases like "walk me through," "to elaborate," "the trade-off between X and Y," and "it depends on." Most non-native speakers have partial coverage across categories. This guide gives you all 80.

How do I describe algorithms in English?

Use this structure: state the algorithm, describe its time and space complexity, explain why it's appropriate for the problem, then describe any trade-offs. Example: "My approach uses a two-pointer technique. The time complexity is O(n) since we traverse the array once, and the space complexity is O(1) since we're not using any additional data structures. The trade-off is that this only works if the input is sorted — which, given the constraints, it is."

What does "walk me through your approach" mean in interviews?

It means: explain your thinking process step by step before you write code. The interviewer wants to hear you narrate your reasoning — how you're breaking down the problem, what trade-offs you're considering, why you're choosing one data structure over another. It's not a signal to start coding immediately. It's an invitation to think out loud. Strong candidates respond with something like: "Sure. Let me start by clarifying the constraints, then I'll outline my approach before writing any code."

How can I sound more confident in English tech interviews?

Three specific techniques: First, use structural phrases that signal organized thinking — "I'll break this into three parts," "the key considerations are X, Y, and Z." These make you sound deliberate. Second, quantify everything — "I reduced latency from 800ms to 90ms" sounds far more confident than "I improved the performance significantly." Third, make your assumptions explicit — "I'm going to assume the input array is sorted; I'll note where that assumption matters." Explicit assumptions signal awareness, not weakness.

What is the STAR method and what vocabulary does it use?

STAR stands for Situation, Task, Action, Result — a framework for answering behavioral interview questions. Situation vocabulary includes: "The context was," "During my time at," "We were in the middle of." Task vocabulary: "I was responsible for," "I owned," "My accountability was." Action vocabulary: "My approach was to," "I escalated to," "I drove the," "I collaborated with." Result vocabulary: "The outcome was a [X]% improvement," "As a direct result," "The impact was." The key ratio: spend 50% of your answer on Action and 25% on Result. Most candidates spend it on Situation, which tells the interviewer very little.

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Conclusion

The engineers who get offers at FAANG are not always the ones who can solve the hardest LeetCode problems. They are the ones who can solve a medium-difficulty problem clearly, communicate their thinking precisely, and make the interviewer confident that they'd be good to work with.

For non-native English speakers, this is a learnable skill. Not a talent. Not luck. Every phrase in this guide was used by a real engineer in a real interview. The vocabulary is finite. The frameworks (STAR, system design structuring, out-loud reasoning) are learnable.

Your 30-day sprint starts with a decision: which vocabulary gap is costing you the most right now?

For engineers actively preparing for technical interviews, Rhythm Word lets you add any of the 80 words from this guide and study them with personalized sentences in the exact register you'll need: professional, technical, and context-rich. The app's spaced repetition engine ensures you review the words you're weakest on, not the ones you already know.

Every word you automate is one less hesitation in the interview room.

Download Rhythm Word on the App Store: Free to Download

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Looking for more professional English vocabulary resources? See also:

• Business English Vocabulary: The Complete Professional's Guide
• English for Job Interviews: 60 Words and Phrases That Get You Hired
• Active Recall: The Fastest Way to Make Vocabulary Stick
• Why Context Sentences Are the Most Efficient Way to Learn Vocabulary