An Insider's Guide to Landing an ML Position in 2025

A hiring manager's insider guide to landing Machine Learning Engineer roles in 2025's evolving job market

After nearly 11 years as a Machine Learning Engineer, hundreds of interviews (both giving and receiving), and experience as a hiring manager across government, startups, and tech giants like Adobe, Twitter, and Meta, I've developed a deep understanding of what it takes to secure an ML position. As we enter 2025, I want to share my insights to help you navigate this evolving landscape.

The Current State of the ML Job Market

As I have mentioned in an earlier newsletter, the tech job market, particularly in machine learning, is showing signs of recovery. However, don't expect an immediate return to the frenetic hiring pace we saw in 2022. This gradual thaw presents both challenges and opportunities for job seekers. The key is to approach your job search strategically and methodically.

Building Your Job Search Foundation

The most successful job searches are well-organized campaigns, not scattered attempts. Here's how to build a strong foundation:

1. Create a Strategic Command Center

Start by creating a centralized tracking system using Notion or Excel. This isn't just about staying organized—it's about treating your job search like a project you're managing. Track:

• Companies you're targeting

• Application status

• Interview schedules

• Follow-up tasks

• Feedback and insights from each interaction

2. Optimize Your LinkedIn for Inbound Opportunities

Think of LinkedIn as your 24/7 job search agent. I've hired numerous MLEs, and it's surprising how many candidates underutilize this platform. (Seriously, who are these jokers?)

Here's what matters:

• Write detailed role descriptions that highlight specific ML projects, technologies, and impact

• Include quantifiable achievements (e.g., "Reduced inference time by 40% through model optimization")

• Use industry-standard keywords that recruiters search for

• Keep your "Open to Work" settings updated and accurate

Remember: Getting approached by a recruiter or hiring manager is infinitely more effective than sending out hundreds of applications.

This is an instance of do as I say, not as I do… but in all seriousness I am not currently applying for any roles, so editing my LinkedIn is not worth my time.

Preparation and Practice

Technical Interview Preparation

Data Structures and Algorithms (DSA) preparation requires a marathon mindset, not a sprint approach. Start early and maintain consistency. Pattern recognition in DSA questions comes from regular practice, not last-minute cramming. (Stay tuned for my detailed post on optimal DSA preparation strategies.)

The Power of Mock Interviews

For DSA specifically I highly recommend you create a study group with peers at a similar level. Alternate between interviewer and interviewee roles. This dual perspective is invaluable—you'll learn both what interviewers look for and how to present your solutions effectively.

Schedule a behavioral/system design mock interview early in your preparation. I know you are terrified of this, but the goal isn't perfection; it's understanding how to:

• Structure your responses

• Manage time effectively

• Handle ambiguity

• Communicate technical decisions

Maximizing Your Applications

The Art of the Cover Letter

People spend too much time personalizing their resume, instead write a personalized cover letters for each position. This isn't about following a template—it's about crafting a compelling narrative that answers: "Why can't this team afford not to hire me?"

Your cover letter should:

• Demonstrate understanding of the company's challenges

• Connect your experience to their specific needs

• Show enthusiasm for their mission and technology

• Be concise yet impactful (2-4 paragraphs)

Strategic Interview Scheduling

Your interview sequence matters. If FAANG companies are your primary target, schedule their interviews last and use other companies for practice and offer negotiation leverage. However, if you're open to any strong opportunity, schedule FAANG interviews first, as their processes typically take longer.

Building and Leveraging Your Network

While cold applications might seem like the most straightforward path, they often face an uphill battle against automated screening systems that can filter out qualified candidates for arbitrary reasons. Think about it: your resume, no matter how impressive, is competing with hundreds of others in a system designed to reduce, not discover, potential matches.

Instead, I've found that investing time in your professional relationships can dramatically change your job search trajectory. Your former colleagues, who know the quality of your work firsthand, can become your strongest advocates.

When I saw hiring at Adobe, a recommendation from a trusted team member would always earn a candidate a careful look at their application, often bypassing initial screening stages entirely. These aren't just referrals from strangers on Blind – they're endorsements from people who can speak to your abilities with credibility.

Reach out to your college alumni network too. The shared experience of your alma mater creates an instant connection, and many professionals feel a genuine desire to help fellow alumni succeed. I've seen countless successful placements happen through alumni connections, often in roles that weren't even publicly posted yet. These conversations can also provide invaluable insights into company culture and team dynamics that you won't find in job descriptions.

Managing the Mental Game

The psychological aspect of job searching is rarely discussed, but in my experience, it's often what separates successful candidates from those who burn out before finding their ideal role. Job searching can feel like an emotional roller coaster – one day you're excited about a promising interview, the next you're dealing with rejection or, sometimes worse, silence.

I've learned to approach this challenge with deliberate strategies for maintaining mental resilience. When a batch of interviews doesn't go as planned, resist the urge to immediately jump into more applications. Instead, take a strategic pause – a few days to step back, analyze what happened, and recalibrate your approach. During my job searches, I've found that these brief breaks often lead to more clarity and better performance than pushing through fatigue.

Consider batching your interviews strategically. When you have multiple interviews in a similar timeframe, you can maintain interview momentum while getting a comparative perspective on different opportunities. However, be careful not to overload yourself – I've seen candidates try to handle five interviews in a week, only to find their performance deteriorating with each one.

Most importantly, build regular "vacation" days into your search schedule. These aren't just breaks from interviewing; they're opportunities to completely disconnect from the job search mindset. Spend time in nature, play video games, or engage in whatever activities help you recharge. I remember taking a three-day hiking trip after a particularly challenging interview series, and returning with renewed energy and clarity that ultimately helped me perform better in subsequent interviews.

Remember, this is a marathon, not a sprint. Every interview, regardless of the outcome, is an opportunity to learn and improve. I've seen candidates transform early interview struggles into valuable insights that led to successful outcomes later in their search. Your journey to finding the right role is exactly that – a journey, with its own unique path and timeline.

Know Your Interview Format

One of the most critical yet overlooked aspects of ML interview preparation is understanding exactly what you'll be asked. Machine learning interviews vary dramatically across companies, and walking in blind can derail even the most qualified candidates. Your first step should always be asking your recruiter for a detailed breakdown of the interview process.

During my time as both an interviewer and hiring manager, I've seen candidates excel in system design but stumble in coding rounds simply because they focused their preparation on the wrong areas. Each company approaches ML interviews differently – some emphasize theoretical knowledge, others prioritize practical implementation skills, and many mix both approaches.

For instance, a startup might focus heavily on your ability to quickly prototype and deploy models, while a larger tech company might drill deep into algorithmic fundamentals. Government positions often emphasize documentation and model interpretability. Understanding these nuances beforehand allows you to prepare strategically.

I've created a detailed video breaking down these different interview types and what to expect in each (I’ve gotten less awkward at YouTube since then).

Understanding these formats ahead of time allows you to allocate your preparation time effectively and avoid surprises during the interview process. Also do not overextend yourself with many companies that ask wildly different types of things.

Remember to ask your recruiter specific questions like:

• How many rounds should I expect?

• What is the focus of each round?

• Should I prepare for system design, and if so, will it be general or ML-specific?

• Will there be any take-home assignments?

• What tools or languages should I be prepared to use?

The most successful candidates I've interviewed aren't necessarily those with the most experience – they're the ones who understood exactly what they needed to demonstrate and prepared accordingly.

Looking Forward

Remember, if you're applying for roles aligned with your experience level, success is a matter of when, not if. The 2025 market may be more measured than previous years, but opportunities exist for well-prepared candidates.

If you're interested in more personalized guidance, I'm launching a mentorship program to help ML engineers navigate their career journeys. Sign up here for updates