Land Your Dream AI/ML Job: A Practical Guide

Want to land your dream AI/ML job? This practical guide breaks down key strategies, from mastering technical skills to optimizing your resume and networking effectively, helping you stand out in the competitive AI job market.

Over the years, both on LinkedIn and in person, many people have reached out to me with the same question: “How do I actually break into AI/ML?” People want to know how to enter this field. Some are software engineers looking to switch careers. Others want to grow into technical leadership. Many are just starting their journey in AI/ML. But everyone wants to know how to succeed in this fast changing field!

In this article, I will speak specifically about a key component of building an AI/ML career: the interview process. This is often one of the most challenging yet crucial steps in transitioning into AI/ML roles.

Having spent over 15 years in AI/ML and served as a Bar Raiser at Amazon, I’ve conducted more than 600 interviews across software engineering, applied science, data science, product and program management, and technical leadership roles. I’ve had the opportunity to hire over 100 professionals across my teams and have been fortunate to see many of them grow into senior levels, leadership positions, and AI/ML roles. Drawing from both my personal experience and countless conversations with aspiring candidates, hiring managers, and recruiters, I’ll share strategies and insights that can help you approach your AI/ML job search effectively.

Whether you’re a seasoned architect looking to lead AI initiatives or a junior engineer eager to break into the field, I hope these learnings and observations about what companies look for will help you position your unique experience in the best possible way.

Where Do You Fit in the ML World?

The variety of AI/ML roles can feel overwhelming at times. While job titles and responsibilities often vary across companies, in my experience, most positions fall into three distinct categories: ML Engineering, Data Science, and Research/Applied Science.

1. ML Engineer: You’ll Build AI Systems That Work in Production- Think of this as being a software engineer with AI superpowers. You’ll take AI models from existing frameworks or those built by your research/applied scientist counterparts and make them work in real products. You may also work on optimizing the model performance and solving scaling bottlenecks. ML Engineers build amazing things – like Netflix-style recommendation systems that help users find their next favorite show, or systems that catch fraud before it happens in banking apps. You’ll work on the entire pipeline: gathering data, deploying models, and making sure everything runs smoothly in production.
2. Data Scientist: You’ll Solve Problems with Data- As a Data Scientist, you’ll be like a detective who uses data to solve business mysteries. Want to know why customers are leaving your product? You’ll analyze patterns in user behavior and build models to predict who might leave next. You’ll crunch numbers to find the perfect pricing strategy that keeps both customers and the business happy. And when someone asks “Does this new feature actually work?”, you’ll design experiments to find out.
3. Applied or Research Scientist: You’ll Push AI Forward- Love diving deep into how things work? As an Applied or Research Scientist, you’ll create new AI solutions. You’ll work on making language models run faster and smarter, teaching computers to see and understand the world better, or finding totally new ways to make AI systems learn. Your mission? Pushing the boundaries of what’s possible while keeping one foot in the real world.

In this article, we’ll zero in on the ML Engineer role. It’s a natural next step for software engineers looking to level up their career with AI/ML skills. Curious about Data Scientist or Research Scientist paths? Stay tuned, I’ll dive deep into those in upcoming articles.

The 7-Step Interview Path for ML Engineers

The interview process really depends on where you’re applying. If you’re interviewing at a startup, they’ll likely focus on specific skills they need right now. Maybe they need someone who knows how to build recommendation systems, or perhaps they’re looking for computer vision expertise for their product. The interviews will be laser-focused on these areas.

Big tech companies, on the other hand, usually cast a wider net. You’ll need to show you can handle different ML challenges. They might ask you about building large-scale ML systems, test your knowledge of NLP or computer vision, and see how you think about scaling solutions to millions of users.

But no matter here you interview, the process usually follows a familiar pattern. If you’ve done software engineering interviews before, you’ll recognize most of the steps. Think of it as a regular software engineering interview, with some extra ML flavor added in.

Based on my experience as an interviewer and from talking with many candidates, hiring managers, and recruiters, I’ve found the ML interview process typically follows 7 steps. Let me walk you through these steps in detail.

Step 1 – Resume Review

Whether you apply directly on a company website or respond to a LinkedIn message, you’ll probably need to submit your resume before speaking with a recruiter. Your resume needs to clearly signal your ML aspirations and capabilities:

• Lead your summary section with explicit interest in AI/ML roles
• Highlight AI/ML projects prominently, even if they were side projects
• List relevant tools and frameworks that you have previously worked (PyTorch, TensorFlow, scikit-learn, etc.)
• Frame non-ML experience as transferable skills (e.g., “Built scalable distributed systems handling 1M+ requests/day” → relevant for ML serving infrastructure)

Step 2 – Recruiter Call

This first conversation sets the trajectory for your interview process. It’s crucial to be clear about your interests and aspirations in AI/ML.

When talking to startups, demonstrate how you connect with their mission and can contribute specific ML solutions to their challenges – they’re looking for people who can hit the ground running with their unique problems.

In larger companies with multiple teams, this is your opportunity to ensure you’re routed to ML-focused positions. Often, recruiters may have both traditional software engineering and ML roles available.

• Be explicit about wanting an ML Engineering role
• For startups: Research their product and suggest specific ways ML can help their vision
• For large companies: Request ML-focused teams specifically
• Be prepared to decline non-ML role options if offered
• Have 2–3 ML project examples ready to discuss briefly
• Connect your experience to their specific ML challenges (e.g., “I see you’re building recommendation systems – I’ve worked on similar problems at scale”)

Step 3 – Initial Technical Screens

The overall goal of technical screening interviews is to ensure that you are “potential fit” for the role. In other words, screening interviews ensure any additional time invested in a full interview loop is worthwhile, both for the hiring team and the candidate.

The process significantly differs by company size. At larger firms, you can expect standardized coding challenges and tests around your ML fundamentals. For mid to senior roles, you can also expect questions on:

• System design capabilities to understand how you think about scalable architectures, data flows, and production considerations specific to ML systems
• Your previous work experience to understand the scope at which you have operated (are you leading people?, are you advising your manager?, are you influencing multiple organizations?)

Startups tend to focus more on open-ended questions to gauge your problem-solving approach in a smaller, fast-paced environment, like designing an MVP for a product.

Step 4 – Take-Home Challenges (Optional)

Startups and newer companies sometimes include take-home challenges as part of their process. They’ll present a real-world problem in their domain and give you anywhere from a few days to a week to complete it. If you’re particularly interested in a startup, research their domain thoroughly before starting the formal interview process!

Step 5 – Full Interview Loop

This is the core and most make-or-break event in your ML engineering interview journey. Pretty much every company has this phase regardless of size or location. Companies generally organize a series of interviews that may last a half-day or a full day:

• ML System Design: You’ll need to design architectures for systems like recommendation engines, fraud detection, ad click prediction, among many others. Startups may look for quick MVPs while larger companies focus on scalability and reliability.
• Coding Challenges: In addition to standard LeetCode type problems, you can also expect data structure problems with ML twists, often focusing on efficient data processing and model serving
• ML Fundamentals: Expect questions around model evaluation, training, inference, and handling real-world challenges like data drift.
• Behavioral: Come prepared with stories of how you’ve worked with data scientists, product teams, and other groups to deliver impactful ML projects. Be ready to share specific examples of what you built, the measurable results, and how you navigated cross-team challenges.

Step 6 – Team Matching and/or Closing Calls

In large companies where the interview process is centralized (common in FAANG and other Big Tech companies), team matching can happen after clearing the technical rounds. This is another crucial opportunity to emphasize your ML interests and impress potential hiring managers.

For startups, this might include a final meeting with C-suite leadership before an offer. Either way, you’re close to receiving an offer. Please take these conversations seriously!

• Do your research about the teams with whom you are talking. For startups, going through their website may be sufficient. For larger companies, you can request the recruiter to share more details beforehand.
• Show enthusiasm for the team’s technical challenges.
• Demonstrate relevant domain knowledge and show why you will be a great fit for the team.
• Discuss specific ML projects you’d be working on.

Step 7 – Final Offer

While not unique to ML roles, this stage typically involves:

• Reference checks
• Background verification
• Compensation negotiation
• Start date coordination

Throughout the process, remember to consistently emphasize your ML focus while demonstrating strong software engineering fundamentals.

Preparing for the Interview

Your preparation phase is crucial – this is where you build the foundation that will carry you through each stage of the interview process. Let me share what I’ve learned from both conducting interviews and helping engineers successfully navigate this transition.

Behavioral Rounds – Demonstrate How Your Software Expertise Transfers

Most candidates underestimate the importance of behavioral interviews in ML engineering roles. As you climb the career ladder, these rounds become increasingly critical to your success. There are 3 key datapoints that interviewers are looking for:

• Your Level of Seniority: Your examples must match the level you’re targeting. I’ve seen many senior/principal engineers fall short by discussing tactical work when we’re looking for strategic impact. If you’re interviewing for a senior engineer or architect role, talk about examples that reflect strategic thinking and impact. For example, talk about a time you led the redesign of your organization or team’s entire system, not just a feature update. Share examples where you’ve influenced architectural decisions, led cross-functional initiatives, or mentored other engineers in adopting ML practices.
• Your Domain Expertise: If you have previously worked on AI/ML projects and are interviewing for an ML Engineer role, highlight these projects prominently. Even if you haven’t worked directly on ML systems, you can demonstrate relevant expertise. For example, if you have worked on optimizing mobile computing resources previously, talk about how this relates to edge ML inference challenges. Similarly, show how your previous work on data pipeline architecture translates perfectly to ML feature engineering at scale. The key is making these connections explicit — don’t assume interviewers will connect the dots for you!
• Aligning with Company DNA: This is where preparation really pays off. Research the company’s engineering philosophy and ways of working (see Amazon’s Leadership Principles, Netflix Culture, or Meta Culture). For startups, go beyond their website – read their technical blog posts, watch conference talks by their founders, and understand their unique challenges. Are they prioritizing rapid experimentation to find product-market fit? Or are they focused on building reliable systems for their enterprise customers? Do they need blazing-fast inference for real-time applications, or are they optimizing for model accuracy in batch processing?

Coding and ML Foundations – Building Your Technical Arsenal

When it comes to the technical rounds for ML Engineers, you need to master both traditional coding challenges and ML-specific problems. Building on the foundational learning path I discussed in my previous article “AI for Software Engineers: Evolve, Don’t Restart” – where I covered structured learning through courses, hands-on projects, and gradual skill development – here are some battle-tested approach to interview-specific preparation:

• Data Structures and Algorithms: Most ML Engineering roles will require strong coding fundamentals. Almost all FAANG type companies will ask you standard algorithmic challenges similar to those in classic software engineering interviews. Resources like “Cracking the Coding Interview,” LeetCode, or HackerRank can help you practice. You should be comfortable discussing time and space complexity, along with tradeoffs.
• ML Fundamentals: You will also want to prepare essential ML concepts. Even if you are not applying for a research-heavy role, be ready to talk about topics like model evaluation (accuracy, precision, recall, F1), overfitting, underfitting, data splits (train, validation, test), and the difference between regression and classification. Some companies may ask about more advanced subjects like Transformers, CNNs, or Diffusion Models. Focus on understanding them well enough to explain how they work at a high level and how you would implement them in a real environment.
• Coding challenges with an ML Twist: While traditional tech companies often stick to LeetCode-style algorithmic problems, I’m hearing about a growing trend, especially among AI-focused startups and newer companies, to ask practical ML coding challenges. These companies want to see if you can actually work with ML tools and frameworks in real-world scenarios.

Pro tip: Always ask your recruiter about the coding interview format. You don’t want to be caught off guard when asked to implement feature engineering in pandas or debug a PyTorch training loop, expecting only traditional algorithmic questions. A simple question like “Will the coding rounds involve working with ML frameworks?” can help you prepare appropriately!

ML System Design – Architecting for Scale and Intelligence

System design rounds are often the make-or-break factor in determining your level. For mid to senior roles, your performance here can be the single most important data point in the hiring decision. I’ve seen strong candidates with perfect coding rounds still get down leveled or rejected based on their system design performance.

Why is it so critical? Because ML system design demonstrates not just your technical knowledge, but your ability to architect complex solutions, make strategic trade-offs, and think holistically about ML applications in production. This is exactly what you’ll be doing day-to-day in senior ML engineering roles.

In a traditional system design interview, you might spend time discussing API design and service orchestration, data schema, database sharding and replication, caching strategies and CDN usage, load balancing and autoscaling, and authentication, authorization, and security best practices

But in ML system design interviews, while these aspects remain relevant, you’ll need to expand your thinking to include:

• Data Pipelines: Think about how data is collected, stored, cleaned, and prepared for training.
• Model Lifecycle: Outline how models are trained, versioned, and retrained based on new data or performance metrics.
• Serving and Deployment: Consider how you deliver real-time or batch predictions. Show understanding of latency requirements, resource constraints, and fallback strategies if models fail.
• Monitoring: Illustrate how you keep track of model drift and trigger updates or rollbacks.

ByteByteGo and Grokking the ML System Design Interview provide excellent structured approaches to ML system design interviews. They’ll help you develop a systematic framework for tackling these complex problems.

But don’t stop there. What really sets candidates apart is their knowledge of real-world ML systems. Read engineering blogs and case studies from both tech giants and innovative startups.

• How does a video streaming platform optimize their recommendation systems?
• How does a food delivery add handle real-time delivery predictions?
• How do ride-sharing app manage their fraud detection systems?
• How are smaller startups innovating with limited resources?
• How does a social-media app show relevant content to their users?

Understanding these real-world implementations helps you speak more confidently about practical trade-offs and challenges. When you can reference how other companies have solved similar problems, it shows you think beyond theoretical patterns to real-world applications.

Startup vs Big Tech: Adapting Your Interview Strategy

I want to re-emphasize how your interview preparation should adapt based on your target company. This ties back to everything we’ve discussed so far about behavioral rounds, coding challenges, and system design.

Startup Interviews – Pragmatic Impact

At startups, interviews will mostly focus on your ability to deliver end-to-end ML solutions with limited resources. The same behavioral example you’d use at a big tech company needs reframing, instead of highlighting how you optimized a massive distributed system, emphasize how you made pragmatic trade-offs to ship quickly.

In technical rounds, expect more focus on practical ML implementation than theoretical concepts. Your system design solutions should prioritize quick iterations and MVP approaches over perfect scalability. Remember those company research tips we discussed? They’re especially crucial here. Understanding a startup’s specific challenges helps you frame answers that resonate with their immediate needs.

Big Tech Interviews – Scale and Depth

Large companies typically have established ML platforms and specialized teams. Your behavioral examples should demonstrate impact at scale. Technical rounds often dive deeper into ML fundamentals, and system design questions frequently focus on handling massive scale and complex infrastructure.

When researching company blogs and papers as we discussed earlier, focus on understanding their ML infrastructure and how different teams interact. This knowledge helps you align your responses with their established practices and demonstrate you can work within complex ML ecosystems.

Looking Ahead

The path to becoming an ML Engineer is both challenging and rewarding. While this guide focuses on ML Engineering interviews, I recognize that some of you might be considering Data Scientist or Research Scientist roles as well. These positions require different strategies – focusing more on statistical rigor, experimental design, and advanced algorithmic knowledge. I’ll provide detailed guides for these roles in upcoming articles.

As you prepare for your interviews, stay curious and keep building. Every system you design, every model you deploy, and every problem you solve adds to your expertise. The field of ML is evolving rapidly, and the best engineers are those who never stop learning!

Feel free to connect with me to learn more insights about ML engineering careers and interview preparation.

Related Reading

• From Code to People: Choosing the Management Path: Navigate the transition from coding to management. Learn how engineering and research leaders can excel in people management while driving technical impact.
• Being Technical as an Engineering Manager: Evolving Beyond the Code: Explore how engineering and research managers can stay technical, drive innovation, lead teams effectively, and balance coding with strategic leadership.
• Staying Technical as a Leader: Practical Strategies: Learn practical strategies for engineering leaders to stay technical while balancing leadership responsibilities. Stay relevant, lead effectively!
• The New Technical Leadership: Embracing AI/ML as a Core Competency: Discover how tech leaders can transition to AI/ML, build foundational skills, grow with their teams, and embrace real-world applications for lasting impact.
• AI for Software Engineers: Evolve, Don’t Restart: Explore how software engineers can evolve into AI/ML roles, leveraging existing skills with practical insights and guidance.

About the article

Disclaimer: 

The views and opinions expressed in my articles are my own and do not represent those of my current or past employers, or any other affiliations.

Publication Note:

This article was originally published on my Medium publication on Feb 4, 2024. I’ve republished it here as part of my ongoing effort to centralize my insights

For the original post and additional context, please visit the original article .