ML engineer: complete hiring guide with job description

The ml engineer job description you write determines whether you hire someone who can ship models or someone who can only train them.

Most companies get this wrong. They copy job descriptions that prioritize research experience and academic credentials over production skills. Then they wonder why their ML initiatives never make it past proof-of-concept.

McKinsey found that AI-related job postings increased 21% between 2018 and 2024, but according to research, most ML models never see production. The gap? Companies hire for the wrong skills.

Why production experience beats publications

I’ve watched this pattern repeat: teams hire brilliant researchers who struggle to deploy anything useful. Not because they lack intelligence, but because production ML engineering is a different job entirely.

Research scientists focus on discovering new approaches and publishing papers. That’s valuable work. But ML engineers handle deployment, scaling, and monitoring - the unglamorous work that actually creates business value.

Here’s what separates them. A researcher might spend months improving model accuracy by 2%. An ML engineer asks whether that 2% matters to users, whether the model can handle production traffic, and how to monitor it when it inevitably drifts.

The job market reflects this split. Data from industry sources shows ML engineers earn significantly more than data scientists because companies finally realized they need people who can ship, not just experiment.

What your job description should actually say

A good ml engineer job description doesn’t bury production requirements at the bottom. It leads with them.

Start with deployment experience. Someone who has taken even one model from concept to production knows more about what breaks than someone who trained a hundred models in notebooks. LinkedIn’s hiring guides emphasize owning projects end-to-end as the key differentiator.

Software engineering skills matter more than you think. Python proficiency isn’t enough. You need someone comfortable with version control, testing, CI/CD, and infrastructure-as-code. The gap between data science and production deployment often comes down to software engineering fundamentals.

Cloud platform experience should be non-negotiable. Whether it’s AWS, GCP, or Azure, your ML engineer needs to understand how to deploy and scale models using cloud services. Industry research shows that MLOps combines machine learning with DevOps practices - you can’t do that without cloud expertise.

Skip the PhD requirement unless you actually need research. Most companies don’t. They need builders.

How to interview for real production skills

Standard ML interviews test the wrong things. Whiteboard algorithm questions reveal whether someone studied computer science, not whether they can debug a model that’s failing in production.

Better approach: give them a broken production scenario.

Model accuracy dropped 10% overnight. What do you check first? How do you isolate the problem? What data do you need? This reveals debugging methodology, which matters more than memorizing backpropagation formulas.

System design questions work better than coding trivia. Ask them to architect a recommendation system that handles millions of requests. They should discuss data pipelines, model serving, caching, monitoring, and graceful degradation. Companies like Meta use these system design rounds to separate junior from senior candidates.

Code reviews matter. Give them existing ML code and ask them to improve it. Do they spot the data leakage? Do they notice the missing error handling? Do they suggest adding monitoring? Real ML work involves reading and fixing more code than writing new code.

The debugging reality nobody talks about

Research on failed ML projects reveals a consistent pattern: models that worked beautifully on test data failed in production. Every single time.

Zillow lost over $300 million when their pricing model bought homes at inflated prices. Amazon scrapped their recruiting tool because it discriminated against women. Netflix’s $1 million algorithm challenge winner never made it to production because the engineering complexity wasn’t worth the marginal improvement.

These weren’t failures of machine learning. They were failures of engineering.

A healthcare model I came across performed excellently in testing but failed when deployed. The issue? It used hospital name as a feature. During training, the model learned that patients at cancer hospitals had cancer. But at inference time, patients hadn’t been assigned to hospitals yet. Classic training-serving skew that any experienced ML engineer would catch during code review.

Your job description should signal that you understand this. Mention monitoring, alerting, and incident response. Talk about handling data drift and model degradation. These aren’t extras - they’re core job requirements.

What this means for your hiring process

Stop optimizing for credentials that don’t predict success. A GitHub repo showing production ML deployments tells you more than a list of publications. A candidate who can explain how they debugged a production model failure is more valuable than one who memorized optimization algorithms.

Your ml engineer job description should filter for builders from the start. Be explicit about production requirements. Mention specific tools and platforms you use. Describe real challenges your team faces.

Most importantly, recognize that ML engineering sits between data science and software engineering. You need both skill sets. Industry analysis confirms that the best ML engineers combine statistical knowledge with software engineering discipline.

The market is heading toward specialization. Projections show the ML engineering field growing substantially, but companies that write better job descriptions will capture the talent that can actually ship.

Write job descriptions that reflect what you actually need. Interview for skills that matter in production. Hire people who have already solved problems similar to yours. The alternative is hiring brilliant people who never ship anything.