Hiring nowApplied AI and machine learning engineering

Machine Learning Engineer

Also called ML Engineer

A Machine Learning Engineer turns trained models into tested, dependable services that run inside real products. They make sure model quality holds up in use and that running it stays affordable.

What they do

They take a model, whether built in-house or adapted from elsewhere, and make it work inside production software. That means writing Python services, wrapping models behind APIs, and handling inputs that never look like the training set. They set up evaluation so quality is measured against the real task, not a benchmark. They manage model versions in a registry, plan releases and rollbacks, and watch latency, failures and cost once traffic arrives. They work closely with data scientists, product engineers and infrastructure colleagues, and they often decide where a model ends and ordinary code begins.

What a strong hire delivers

In the first months, a strong hire gets one model into production behind a tested service, with a release package the team can repeat. They put an evaluation set and checks in place so changes can be judged on task quality. They make reliability visible through monitoring and clear failure handling. They also give the company a straight view of operating cost per request, and a plan to reduce it.

How you can tell they are good

Ask them to walk through a model they shipped, end to end. Listen for what broke after release and what they changed. Ask how they knew quality was good enough, and who agreed that standard. Strong people describe tests, rollback plans and cost trade-offs without prompting. Be wary of those who only talk about training accuracy or notebooks.

Why it is hard to fill

The title covers very different jobs. Some candidates are researchers who have never owned a service. Others are good backend engineers who have only called a model API. The people you want sit across both, with production evidence on each side. Companies also use the title loosely, so CVs look similar and tell you little. We usually have to probe the actual work done, not the job title.

Where to find them

Look at software engineers who moved into model work, and applied ML engineers who spent time owning deployment. Common titles include backend engineer, data scientist with production duties, MLOps engineer and research engineer. They come from product companies with live ML features, platform teams, and startups where one person covered the whole path from model to release.

Indicative pay

Not enough published evidence yet to give a fair range for this role, which is common for work this new. We benchmark it for you as part of a search.

Interview questions for a Machine Learning Engineer

  1. Tell me about a model you took into production. What changed between the notebook and the live service?

    A strong answer covers data differences, packaging, latency, failure handling and testing, with specific examples. Worry if they describe only training work, or credit the platform team for everything that made it run.

  2. How did you decide a model was good enough to release?

    Look for task-based evaluation, a defined threshold agreed with the product owner, and checks on edge cases. Worry about reliance on a single aggregate metric or a decision made by feel.

4 more questions, with what a strong answer covers, are in the Role Kit.

Job description you can post

We are looking for a Machine Learning Engineer to put models into production. You will build the services, tests and release processes that make our models dependable in real products, and keep quality and running cost under control as usage grows.

What you will do

  • Implement and integrate models into production software
  • Build and maintain tested model services behind clear APIs
  • Set up evaluation that measures quality on the real task
  • Manage model versions, releases and rollbacks
  • Monitor reliability, latency and operating cost
  • Work with data science and product engineering on design choices

What you will bring

  • Production Python code you can show or describe in detail
  • Experience shipping a learned model into a live service
  • Working knowledge of deep learning with PyTorch
  • Experience with model registries and release practices
  • Evidence of designing evaluation for a real task
  • Clear written and spoken communication with mixed teams

Nice to have

  • Experience with agent frameworks
  • Cost optimisation work on model inference
  • Background in applied ML research

Free to use and adapt. If it saves you time, a link back to this page is appreciated.

Skills that matter

  • PythonAdvancedDevelop tested numerical and data software
  • Deep learningAdvancedBuild and diagnose trainable neural models
  • MLOps and model lifecycleWorkingVersion, release, monitor and retire models
  • Model evaluationWorkingMeasure capability and failure modes credibly

Other roles in applied AI and machine learning engineering

Hiring nowAI Application EngineerAn AI Application Engineer builds the user-facing software that joins a model to business logic. They turn model capability into a product that…Hiring nowApplied AI ScientistAn Applied AI Scientist takes a research method and makes it work on a real product problem. They prove it beats a simple baseline on representative…Hiring nowForward Deployed AI EngineerA Forward Deployed AI Engineer works inside a customer's environment to get AI solutions running on real tasks. They matter because a model only…

Related roles in other families

Hiring nowPost-Training Research EngineerPost-training and preference optimisationA Post-Training Research Engineer adapts pretrained models into ones that behave as the company needs. They turn preference data and reward signals…Hiring nowReward Modelling ResearcherPost-training and preference optimisationA reward modelling researcher builds models that learn what good output looks like from preference comparisons. They give post-training teams a…Hiring nowReinforcement Learning Research EngineerPost-training and preference optimisationA Reinforcement Learning Research Engineer improves how a model behaves by training it against interactive signals. They matter because reward gains…Hiring nowSpeech Recognition EngineerSpeech, audio and music intelligenceA Speech Recognition Engineer builds the system that turns spoken language into accurate text for real users in real conditions. It matters because…

How we wrote this

Role definitions are our own editorial work, checked against occupational classifications, standards, research and employer pages. Titles vary by company, and this is a description of the work, not a job advert.

← Applied AI and machine learning engineeringAll new job families

Start a search

Hiring a Machine Learning Engineer?

Tell us the work. We find the people already doing it, whatever their job title says, and you meet them already interviewed for your role.