A machine learning engineer job description should read like a software engineering posting with models in it, because that is the job. The template below emphasizes production systems over research; adjust the specialty brackets for your use case.
branch of artificial intelligence
Machine learning is the branch of artificial intelligence in which systems learn patterns from data rather than following explicitly written rules, covering supervised, unsupervised and reinforcement approaches.
Delivering it in production is mostly engineering: collecting and labelling data, training and evaluating honestly, serving predictions at acceptable latency, and monitoring for the drift that arrives once inputs change.
Foundation models cover many tasks that used to need custom training, so the sharpest skill is deciding what to train, what to call through an API and how to prove either one works.
Paste the template below into your job board or careers page, then replace the bracketed placeholders with your company's specifics. Every section is written to be edited, not just admired.
About the role [Company] is hiring a Machine Learning Engineer to own the path from trained model to production system for [use case]. Our data science team produces promising models; your job is to make them reliable, fast, and affordable in front of real users. What you will do - Build the serving, monitoring, and retraining infrastructure our models live on. - Turn notebooks into tested, deployable services. - Watch latency, cost, and drift like the production metrics they are. - Partner with data scientists early so models are built to ship. - Raise the engineering bar across our ML codebase. What we are looking for - 3+ years of engineering with models deployed to production traffic. - Strong Python and real software craft: tests, types, review. - MLOps tooling experience you chose and can defend. - Data pipeline competence, because that is where it breaks. - Plain-language communication about technical trade-offs. Nice to have - LLM inference optimization, GPU infrastructure, or [specialty] depth. Compensation and benefits Base salary of $130,000 to $210,000 depending on experience, plus [equity/bonus] and benefits. Calibrate before posting. Why join [Company] You will have real compute, real data, and a data science team that respects engineering constraints. Model infrastructure is a first-class roadmap item here, not a side quest, so your work compounds across every model we ship. Benefits include [equity, health coverage, learning budget]. How to apply Apply with a resume and a description of one model you took to production: the serving stack, the monitoring, and the incident that taught you the most.
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It leads with engineering. Serving infrastructure, pipelines, and reliability replace exploratory analysis and experimentation. Candidates should be evaluated primarily as software engineers.
Base salaries generally run $130,000 to $210,000, at the top of the engineering pay scale, because production ML skills remain scarce. Contract rates typically fall between $55 and $165 per hour.
Probably not yet. An AI-focused product engineer covers API integration well. Hire an ML engineer when you fine-tune, self-host, or run classical ML at meaningful scale.
Most commonly strong backend engineers who moved toward ML, rather than researchers who moved toward code. Weight production engineering evidence over publication lists for this role.
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