OpenAI Software Engineer Interview (process, questions, prep)

By Timothy Agbola Last updated: March 04, 2026 How we wrote this article

If you're preparing for the OpenAI software engineer interview, this guide covers everything you need to know. You’ll learn the full interview process and what OpenAI looks for, plus example questions and a preparation plan.

OpenAI is among the most competitive engineering employers worldwide right now. The company is growing fast, compensation is well above industry average, and the work sits at the center of the AI industry's most consequential developments.

This combination makes OpenAI a target for top engineering talent globally. The interview process reflects that.

To put together this guide, we gathered insights from hundreds of real OpenAI candidate reports on Glassdoor and Blind, plus reliable resources like Levels.fyi and OpenAI's own interview guide.

Here's what we'll cover:

Role and salary
Interview process and timeline
Example OpenAI software engineer interview questions
Interviewing tips
Preparation plan

Click here to practice one-on-one with former OpenAI software engineer interviewers.

1. OpenAI software engineer role and salary ↑

Before we dive into the interview process and questions, let's quickly cover some basics about the role you're applying for.

1.1 What does an OpenAI software engineer do?

Software engineers at OpenAI build and maintain the systems behind products like ChatGPT and the OpenAI API. This infrastructure is used by millions of people.

Depending on the team, an OpenAI software engineer’s work spans a wide range of areas:

Model training infrastructure and compute optimization: building and scaling the systems that train OpenAI's models, including managing GPU clusters and reducing training costs
Product engineering for consumer and API facing products: developing the features and backend systems that power ChatGPT and the OpenAI API for hundreds of millions of users
Safety and alignment systems: building technical safeguards that ensure models behave reliably, predictably, and in line with OpenAI's mission
Developer platform and tooling: creating the APIs, SDKs, and internal tools that let both external developers and OpenAI's own teams build on top of the models

It is worth noting that many engineering roles at OpenAI carry the title "member of technical staff" (MTS) rather than "software engineer." The two titles refer to the same type of role at the company, and the interview process and questions are substantively identical. Everything in this guide applies to both.

Is the OpenAI software engineer role the right fit for you?

The AI industry is new and rapidly growing. As research breakthroughs happen, priorities and product demands will shift, sometimes week to week. As an engineer, you're expected to keep up with its fast-paced nature.

The OpenAI SWE role is for you if you can:

Take ownership with minimal direction. You won't be handed a tightly scoped ticket. You'll be expected to identify what needs doing and drive it.
Work cross-functionally. You’ll be collaborating with researchers, product teams, and safety specialists. You'll need to explain technical decisions clearly to people with very different areas of expertise.
Tackle novel problems with technical finesse and analytical judgment. Much of what OpenAI's engineers work on has no established playbook. You'll regularly encounter situations where the right solution isn't obvious and where your judgment matters as much as your technical ability.

If you do your best work with real autonomy and genuine challenges, it's a strong fit. But if you prefer structured environments with predictable priorities, working at OpenAI may feel out of your depth.

1.2 What does OpenAI look for in software engineer candidates?

OpenAI doesn't publish a formal list of what they evaluate candidates on. What follows is our best picture of it, drawn from OpenAI's own interview guide, our research, and the experiences of hundreds of candidates on Glassdoor and Blind. Though not an official rubric, here are the themes that come up most consistently:

Technical ability. For engineering roles, this means writing clean, maintainable code with strong performance instincts and good test coverage. OpenAI consistently emphasizes production-quality code, not just code that produces the right output.
Problem-solving under uncertainty. OpenAI's interview questions are deliberately more ambiguous than those at most companies. Interviewers want to see how you clarify requirements, make reasonable assumptions, and handle incomplete information, not just whether you can reach the correct answer.
Communication and collaboration. You will need to explain your reasoning clearly throughout each interview and respond well to follow-up questions and changing constraints. The ability to work across research, product, and safety teams is a genuine requirement at OpenAI, not a formality.
Mission alignment. OpenAI is not credential-driven, but it cares deeply about whether candidates understand and genuinely share its mission around building safe and beneficial AI. Expect to be asked about this directly and be prepared to speak to it with real specificity.

1.3 OpenAI software engineer salary and compensation

OpenAI’s generous compensation package is probably one of the reasons you’re interested in working at the company.

Here are the average salaries and compensation for the different software engineer levels at OpenAI. This is based on the reported data from Levels.fyi.

OpenAI SWE Salary Chart

Compensation depends on many factors, but it typically increases with seniority. Equity packages are a particularly significant part of total compensation at OpenAI, given the company's growth trajectory in recent years.

Ultimately, your interview performance will largely determine your offer, which is why working with our ex-OpenAI interview coaches can be a smart investment.

As with all top tech companies, compensation is negotiable. If you receive an offer, don't be afraid to ask for more. You can use our guide to the best salary negotiation services to help you prepare, or work directly with one of our salary negotiation coaches.

2. OpenAI software engineer interview process and timeline ↑

OpenAI Interview Process

OpenAI's software engineer interview process typically takes two to eight weeks and follows these steps:

Resume screen
Recruiter call
Hiring manager screen
Skills-based assessment
Final interviews (4 to 6 rounds)
Decision and offer

One thing to know upfront: OpenAI’s software engineer interview process is decentralised.

The steps above reflect the most common path for software engineers, but your recruiter is your best source of truth for what your specific loop will look like. They'll typically brief you before each stage.

For a full breakdown of every stage, see our OpenAI interview process guide.

2.1 Resume screen

The first step of OpenAI's interview process is the resume screen.

This is an extremely competitive step. Like at other FAANG companies, the vast majority of candidates don't make it past the resume screen.

Here, after you've submitted your application through the OpenAI careers portal, or been contacted directly via email or LinkedIn, recruiters will evaluate your resume to see if your experience aligns with the open position.

To help you put together a targeted resume that stands out, follow the tips below:

Tips on crafting a resume

Study the job description: The work experience you showcase on your resume should directly relate to the role qualifications. For software engineers applying to infrastructure teams, emphasize systems-level work and scale.
Be specific: Use data to back up your claims. How many servers did you manage? How much did you improve latency? Quantify as much as you can.
Emphasize impact and scale: OpenAI values people who have worked on systems and problems at a significant scale. Highlight work affecting millions of users, massive data processing, or complex distributed systems.
Be concise: Recruiters often don't have much time to study a resume in depth, so keep it clear and concise, emphasizing roles and achievements that make you stand out.

For practical guidance and real examples, see our software engineer resume guide. Or, get expert feedback from our team of former FAANG recruiters, who will cover what achievements to focus on (or ignore), how to fine-tune your bullet points, and more.

2.2 Recruiter call

If your resume passes the resume screen, an OpenAI recruiter will reach out to you to schedule a call. This generally lasts 30–45 minutes.

This call is non-technical in most cases. You should expect questions like "Tell me about yourself," "Why OpenAI?" and "Walk me through your resume." Be prepared to discuss your previous experience and explain your motivation for applying to OpenAI.

You may be asked to discuss your work and academic experience, motivations, and goals. Familiarize yourself with OpenAI's recent work, too, especially updates related to the team you're interviewing for. You can find OpenAI's latest research and product updates on their blog.

During this call, the recruiter should give you information on how the overall interview process will work and may answer your questions about the timeline, location, or job description. This is when you should ask about which teams are hiring and express any role preferences.

Your recruiter should also provide you with helpful interview prep materials. If all goes well, the recruiter will get back in touch with you to schedule the skills-based assessment.

2.3 Hiring manager screen

This is a 20 to 30-minute virtual call, typically with the engineering manager for the team you'd be joining. The conversation is a mix of your background, your long-term goals, and light technical discussion.

You may be asked about past systems you've designed, how you've handled technical trade-offs, or your experience with a specific technology relevant to the team.

If you're applying for a role with a research or applied AI component, be ready to discuss past projects in some depth, including your architecture decisions and what you'd do differently today.

2.4 Skills-based assessment

Within about a week of your recruiter call, assuming there is a fit, you will progress to the skills-based assessment. This is where technical evaluation begins in earnest. Your recruiter will provide prep materials before this stage, but the format varies by team and role.

For software engineers, it most commonly involves one or more of the following:

Coding challenge: A practical coding problem on a platform like HackerRank or CoderPad. OpenAI's coding problems are more practical and real-world focused. For example, debugging an existing function or optimizing code for readability and efficiency.
System design interview: For more senior roles, you may face a system design problem where you architect a solution to a technical challenge, including API design, database schema, and overall architecture. These questions can be highly detailed with specifications and mockups provided. Learn more in our OpenAI system design interviews guide.
Take-home assignment or pair coding: Depending on the role or team, you might receive a take-home project or participate in a pair coding session where an engineer observes your problem-solving approach.

If you receive a take-home assignment, the format typically involves a self-contained engineering problem delivered via HackerRank, which you complete asynchronously. These tend to be more open-ended than live coding questions.

Expect something like designing and implementing a small system or refactoring a piece of code with deliberate flaws. Unlike live rounds, you won't have an interviewer to bounce ideas off, so your ability to make reasonable assumptions and document them clearly matters more here.

You can expect to hear back within about a week after completing your assessment. If you advance, the recruiting team will schedule your final interviews.

To see a breakdown of the kind of questions you're likely to face in your skills-based interviews, check out our comprehensive guide to OpenAI interview questions by role (with answers).

You can see detailed example questions in Section 3 below.

2.5 Final interviews

Candidates who pass the skills-based assessment advance to the final interview round.

According to OpenAI's official interview guide, this stage typically involves 4 to 6 hours of interviews with 4 to 6 people over one to two days. Interviews are conducted virtually by default, though you can choose to interview on site at OpenAI's San Francisco office.

The final loop is more comprehensive than the skills assessment.

Even if you solve a problem, it's considered a failure if the interviewer has to give you multiple substantive hints or point out logical flaws you missed. You need to demonstrate independent problem-solving and attention to detail.

You can expect to go through the following interview types:

1. Coding interview(s)

You'll solve one or more coding problems, often in different formats than the skills assessment. Rather than simple implementation problems, final round coding may include code refactoring challenges or more complex practical problems.

Treat your solution as the start of the conversation. Interviewers will ask follow-up questions even after you reach the correct answer.

Learn more about how to get better at coding interviews here.

2. System design or architecture interview

For most mid-level and senior roles, you'll face at least one system design interview during the final round. You're expected to discuss trade-offs thoughtfully, design APIs and database schemas, consider scalability and reliability, and even suggest improvements and features sometimes.

Dive deeper into OpenAI system design, generative AI system design, and machine learning system design interviews.

3. Code refactoring interview (L5 and above)

Senior candidates may face a code refactoring round, where you're given existing code and asked to improve, refactor, or optimize it. Unlike a standard coding interview, you're not building from scratch. The focus is on your ability to read and improve someone else's work under time pressure.

4. Project deep-dive and presentation (L5 and above)

Senior candidates may also be asked to prepare a short technical presentation on a significant past project. You'll walk through your decisions, trade-offs, and the impact of your work. It's OpenAI's way of assessing whether you can communicate complex engineering work clearly and tell a compelling story about it.

5. Behavioral and culture fit interview

Mission alignment matters a lot at OpenAI. Be prepared to discuss your genuine perspective on AGI safety, alignment, and beneficial AI development. You don't need to be an alignment researcher, but show thoughtfulness about the responsibility of building powerful AI systems.

Once you clear the final interviews, it's time to wait for the decision.

2.6 Decision and offer

OpenAI typically moves quickly after the final round, with most candidates hearing back within a week, but that could be longer.

The process tends to move faster when you communicate that you have competing offers. Candidates on Blind consistently report that mentioning a deadline from another company prompts OpenAI to accelerate.

If you've completed the final round and are approaching a decision deadline elsewhere, it's reasonable to email your recruiter with a specific date and ask for an update. Without that prompt, radio silence between stages is common.

If you advance, you will move into offer and negotiation. Team matching can occasionally add some time to this stage before a formal offer is extended.

3. OpenAI software engineer interview questions ↑

There are three types of interviews you will encounter as an OpenAI software engineer candidate:

Coding questions
System design questions
Behavioral questions

We analyzed hundreds of questions reported by real OpenAI software engineer candidates on Glassdoor, Blind, and other candidate forums.

Below, you will find curated lists of sample questions for each interview type.

3.1 Coding questions ↑

OpenAI’s coding interview questions are practical, work-oriented problems that simulate real engineering tasks. Each round typically runs 45 to 60 minutes in CoderPad and involves significant follow-up questions from the interviewer mid-solution.

A working solution is not enough on its own. Interviewers are specifically looking for production-quality thinking. That means:

Clean structure: code should be logically organised into functions or classes, not written as one long block that's hard to follow or test.
Clear variable naming: avoid x, temp, or res. The names should communicate intent so the interviewer can read your code without asking what things mean.
Explicit edge-case handling: proactively account for empty inputs, nulls, boundary values, and unexpected data types without waiting for the interviewer to prompt you.
Test coverage, even when not asked: write at least a few test cases after solving the problem. It signals that you think about correctness the way a working engineer would, not just someone completing an exercise.

The questions below span a wide range of difficulty and depth. Based on our analysis of the most recent SWE coding questions reported on Glassdoor, here are some sample coding questions organized by type:

Example coding questions asked at OpenAI software engineer interviews:

1. Hash tables and caching

Implement an LRU cache that supports get(key) and put(key, value), evicting the least recently used entry when capacity is exceeded.
Implement a key-value store with get, set, and delete operations where values are lists of tuples. Follow up: add sorting and merging across entries.
Implement a versioned key value store that supports put(key, value), get(key), and get(key, timestamp), returning the value for a key as it existed at a given point in time.

2. Graphs and trees

Given a list of words sorted according to the rules of an alien language, determine the order of characters in that language.
Given an n×n grid of 1s and 0s, return the number of islands.
Given the root of a tree, count the number of nodes that satisfy a given condition.
Topologically sort a directed acyclic graph and detect cycles.

3. Practical utilities and parsing

Implement a cd command. Given a current working directory and a target path that may include . and .. components, return the resulting absolute path. Follow up: add symbolic link resolution using a provided mapping dictionary.
Implement a dependency version checker. Given a set of packages and their version constraints, determine which versions satisfy all constraints.
Implement an Excel-style formula evaluator that handles cell references such as =A1+B2. Follow up: detect and handle circular references.
Given a user's GPU usage data and a pricing rules table, implement a calculator that returns their remaining credit balance.
Given a UI mockup with CSS and a data API already provided, implement the interface in code.

4. Object-oriented programming

Implement a simple ORM layer with classes and methods to save, query, update, and delete objects from a database.
Implement a resumable iterator for a large dataset that supports next(), pause(), and resume(), pausing mid-traversal and resuming from the exact position it left off.
Implement a data loader class and write the tests necessary to validate its behavior.

5. Concurrency

The following code has a race condition. Identify it, fix it, and explain your choice of synchronization primitive.
Implement a concurrent web crawler. Starting from a seed URL, fetch pages using multiple worker threads, avoid visiting any URL more than once, and handle fetch failures gracefully.

6. Debugging and refactoring

Note: This topic tends to appear more at senior levels, where you're expected to reason about someone else's code under time pressure.

You are given a function that parses log files but runs in O(n²) time. Debug it, improve its time complexity, and refactor for clarity without changing its behavior. Walk through how you would test your changes.

For more coding practice, see our coding interview prep guide and our guide to how to answer coding interview questions.

3.2 System design questions ↑

For early-career candidates at L2–L3, system design is rarely a focus. Coding is the main technical evaluation at this level, and you're unlikely to face a full system design round.

That said, it's worth having a basic familiarity with system design concepts. Some interviewers may ask lighter architecture questions as part of a broader technical conversation, even at junior levels.

The questions below are most relevant for L4 and above, but we recommend you practice with them anyway, just to cover all your bases.

Example system design questions asked at OpenAI software engineer interviews:

1. Consumer scale product systems

Design Slack.
Design TikTok.
Design Yelp or a notifications service?

2. Platform and infrastructure systems

Design GitHub Actions from scratch.
Design a webhook delivery system. Cover URL delivery, caching, database schema, and failure handling with a retry mechanism.
Design an API for an in-memory database.
Design a rate limiter for a public API service.
Design a fault-tolerant web crawling service that scales to 10 million requests per second.

3. AI native systems

Design the infrastructure to serve ChatGPT at hundreds of millions of weekly users.
Design an LLM-powered enterprise search system that supports natural language queries across internal documents with role-based access control.
Design a GPU scheduling system to allocate compute resources across competing workloads at scale.
Design a vector database to store and search billions of embeddings efficiently.
Design a system to detect NSFW content in real-time ChatGPT outputs. Address model selection, latency requirements, and the feedback loop.

For a detailed breakdown of how to approach this round, including example answer outlines specific to OpenAI, see our OpenAI system design interview guide.

3.3 Behavioral questions ↑

For the behavioral interview, and sometimes at the beginning of your other interviews, you'll be asked behavioral or "resume" questions.

These questions focus on your past work experience, your qualifications, and your motivation for applying to OpenAI. In other words, it's a way for your interviewer to get to know you better.

OpenAI interviewers typically look at these traits in engineers:

Conflict resolution: the ability to navigate disagreements with peers, managers, and external teams constructively and without escalation
Growth mindset: openness to feedback and a demonstrated track record of acting on it
Comfort with ambiguity: the ability to bring structure to unclear situations and make progress without waiting for complete information
Resilience: sustained output and focus when facing repeated obstacles or shifting priorities
Communication: clear, efficient communication with peers, cross-functional partners, and the wider team across different levels of technical depth

Here, "Why OpenAI?" is the most repeated question you’ll face. Interviewers are looking for genuine alignment with the company's mission around safe and beneficial AI.

Be ready to drill down into the technical details of the projects on your resume and discuss the types of projects you'd like to work on in the future. Having clarity in these areas will help you make a strong impression in behavioral interviews.

A rehearsed answer about career growth or the prestige of the role will not land well here. The stronger and more specific your answer, the better the impression you will leave across the rest of the loop.

Example behavioral questions asked at OpenAI software engineer interviews:

1. General

Tell me about yourself.
Tell me about a project or accomplishment you are most proud of.
Why do you want to work at OpenAI?
Walk me through a large technical program or initiative you have led or been heavily involved in.

2. Technical problem-solving

Tell me about a time you solved a particularly complex technical problem. What was your approach?
Describe a situation where you had to make a difficult technical trade-off decision.
Tell me about a time you influenced a technical decision without having direct authority over it.

3. Collaboration and conflict resolution

Tell me about a time you made a mistake. What happened and what did you take away from it?
How do you manage multiple conflicting priorities?
Tell me about a time you and a cross-functional partner disagreed. How did you resolve it?

For detailed frameworks and example answers to all of these questions, see our guide to OpenAI interview questions by role.

4. OpenAI software engineer interviewing tips ↑

You might be an excellent software engineer, but that alone won't be enough to ace your interviews at OpenAI. Interviewing is a skill in itself that you need to develop deliberately, and OpenAI's style has specific characteristics that are worth preparing for.

Let’s look at some key tips to make sure you approach your interviews the right way.

1. Ask clarifying questions before you write any code.

OpenAI's coding problems are intentionally more ambiguous than standard algorithm questions. Spend the first few minutes of each coding round clarifying scope, asking about edge cases, and restating the problem in your own words. Interviewers expect and sometimes reward this.

2. Write code as though it is going into production.

OpenAI's coding bar is higher than at most companies because interviewers want to see production-quality thinking, not just a working solution. Aim for clean structure, sensible naming, explicit edge case handling, and good test coverage.

3. Think out loud and be collaborative.

Walk your interviewer through your reasoning as you work, not just once you have reached an answer. OpenAI wants to understand how you think under pressure, so narrate your decisions, state your assumptions, and flag when you are making trade-offs.

4. Go brute force first, then iterate.

Do not chase the perfect solution before you have a working one. Get to a correct answer first, then discuss how you would improve it. Most OpenAI interviewers appreciate a candidate who can clearly explain trade-offs at each step far more than one who freezes trying to find the optimal approach from the start.

5. Center your behavioral answers around the mission.

OpenAI is unlike most tech companies in how central its mission is to hiring decisions. Familiarize yourself with the OpenAI Charter, read the OpenAI research blog, and form genuine views on what the company is building and why it matters. Vague answers about being excited by AI or wanting to work on interesting problems will not be convincing here.

6. Know your past projects in depth.

Be ready to discuss architecture decisions, what you would do differently today, how you measured success, and what you learned from failures along the way. Interviewers will probe hard, so choose a project you know inside out.

7. Communicate efficiently

During your technical interviews, you’ll also be assessed on your communication skills and how you talk through your problem-solving process. Use this as your opportunity to show how well you communicate and collaborate with colleagues by treating your technical interviews like a conversation.

In system design interviews, start by stating your assumptions to make sure you’re aligned with your interviewer. And in behavioral interviews, set up your situation quickly before diving into your actions and results.

A great way to improve this skill is to familiarize yourself with answer frameworks, such as IGotAnOffer’s SPSIL framework (Situation, Problem, Solution, Impact, Learning) for behavioral interviews, or the 4-step system design framework we recommend to candidates for tackling system design questions.

8. Call out trade-offs in real time.

Every design or implementation decision you make has a trade-off. Don't wait for the interviewer to ask; name them as you go.

"I'm using a hash map here for O(1) lookups at the cost of extra memory" is exactly the kind of thinking OpenAI interviewers are evaluating. If you don't surface trade-offs, they will prompt you anyway, and it's better to show you got there independently.

5. Preparation plan ↑

Now that you know what questions to expect, let's focus on preparing as efficiently as possible. Below are the four steps we recommend.

5.1 Deep dive into OpenAI's culture and mission

Before investing dozens of hours in technical prep, take the time to understand what OpenAI is actually building, why it matters, and whether it genuinely aligns with what you want to work on.

This matters at OpenAI because the mission is not just a marketing statement. It shapes how the company hires, how teams operate, and how decisions get made at every level. Interviewers will probe your alignment with it directly.

Here are some resources to get you started:

If you know engineers who currently or formerly worked at OpenAI, speaking with them directly is worth the effort. Glassdoor and Blind also have candid employee reviews that give a realistic picture of day-to-day life at the company.

5.2 Practice by yourself

As we've outlined above, you will have three types of interviews at OpenAI: coding, system design, and behavioral. The first step in your preparation should be to build a solid foundation in each area and practice answering questions independently.

For coding interviews:

Practice in CoderPad or a plain text editor without autocomplete; that's the environment you'll be working in. Focus especially on data structures, OOP design, concurrency, and debugging:

Coding interview prep guide: a step-by-step framework for approaching coding problems, with practice questions and worked examples.

For system design interviews:

OpenAI's rounds often require familiarity with ML infrastructure at scale, so generic prep isn't enough. These guides contain question breakdowns specific to what OpenAI asks:

How to answer system design questions: a minute-by-minute breakdown of how to structure your answer from start to finish.
OpenAI system design guide: question breakdowns and tips specific to what OpenAI asks.
Machine learning system design interview: how to approach ML-specific system design questions, which come up regularly at OpenAI.

For behavioral interviews:

Prepare specific, detailed stories for the questions in Section 3.3. Treat "Why OpenAI?" as one of the most important to get right, as a vague answer is a red flag:

Proven method for answering behavioral questions: IGotAnOffer's SPSIL framework, with example answers.
Common software engineer behavioral questions: the questions that come up most frequently in SWE interviews, with sample answers.

For OpenAI-specific preparation:

To go deeper on each area, we've put together a set of OpenAI-specific guides:

OpenAI interview process: a detailed breakdown of every stage, from recruiter screen to final round.
OpenAI interview questions: coding, system design, and behavioral questions reported by real candidates.
OpenAI system design interview: what the system design rounds involve at OpenAI and how to approach them.

A great complement to written practice is answering questions out loud. It sounds unusual, but it will significantly improve how you communicate under pressure during the real interview.

5.3 Practice with peers

If you have friends or colleagues who can do mock interviews with you, that is a worthwhile option to explore. The experience of performing in front of another person is genuinely useful and it is free. That said, peer practice comes with real limitations:

It is hard to know whether the feedback you receive is accurate.
Your peers are unlikely to have any inside knowledge of OpenAI's specific interview style.
On peer platforms, people often do not show up or do not take the sessions seriously.

For those reasons, many candidates move straight to practicing with an expert.

5.4 Practice with experienced former OpenAI interviewers

In our experience, practicing real interviews with experts who can give you company-specific feedback makes a significant difference in your outcomes.

Working with an OpenAI interview coach means you can:

Practice under conditions that closely simulate the real interview
Get accurate, detailed feedback from someone who has been on the other side of the table
Build your confidence and composure with OpenAI's specific style of follow-up questions
Get company-specific insights you simply cannot find in public prep materials
Learn how to structure and tell your best technical stories
Keep your preparation focused so you are not wasting time on the wrong things

Landing a software engineering role at OpenAI often results in a $50,000 per year or more increase in total compensation. In our experience, three or four coaching sessions at around $500 make a meaningful difference in your ability to get the offer. That is an ROI of 100x.

Click here to book software engineer mock interviews with experienced SWE interviewers.