This free Machine Learning Engineer job description template is ready to use — copy it, replace the {{placeholders}}, and post your role in minutes. It includes a company intro, a role summary, responsibilities, requirements, nice-to-haves, and compensation, with writing tips and FAQs below to help you tailor it to your team.
When to use this template
Use this when you're hiring an engineer to take machine learning models into production and keep them running — building the pipelines, serving infrastructure, and monitoring that turn a model into a reliable product feature. It's an engineering role with ML depth, not a research role.
ML engineering candidates want to know how mature your ML practice is, what you're building (recommendations, search, LLM features, etc.), and the line between this role and data science. Be specific.
If the role is mainly building and validating models, use the Data Scientist template; if it's general backend work, use the Backend Engineer template.
Writing tips
- Describe what you're building with ML and how mature your ML stack is.
- Clarify the boundary between this role and data science.
- Emphasize production engineering — serving, pipelines, monitoring — not just modeling.
- Name your ML and infrastructure tooling.
- Include the salary range and seniority level.
The job description
Copy the template below and replace the {{placeholders}} and [bracketed notes] with your specifics.
About {{company}}
{{company}} is [what you do]. Machine learning powers [feature/area], and we're hiring a Machine Learning Engineer to take models from notebook to production and keep them reliable.
The role
As a Machine Learning Engineer, you'll productionize machine learning — building the pipelines, serving infrastructure, and monitoring that turn models into dependable features. You'll work closely with data scientists and backend engineers. This role reports to {{hiring_manager}} and is based {{work_type}} in {{location}}.
What you'll do
- Take models from prototype to reliable, production-grade systems.
- Build training and inference pipelines and serving infrastructure.
- Monitor models in production for performance, drift, and reliability.
- Partner with data scientists to operationalize their work.
- Improve the tooling and platform that supports ML across the team.
What we're looking for
- 3+ years in machine learning engineering or backend engineering with ML.
- Strong software engineering fundamentals and proficiency in [Python].
- Experience deploying and operating models in production.
- Familiarity with ML frameworks ([PyTorch, TensorFlow, scikit-learn]).
- A pragmatic focus on reliability, latency, and cost.
Nice to have
- Experience with MLOps tooling and feature stores.
- Background working with LLMs or your relevant model types.
- Strong cloud and infrastructure skills.
What we offer
- Salary range: {{salary_range}}, plus equity.
- [Comprehensive benefits].
- Flexible {{work_type}} working and [PTO policy].
- The chance to ship ML that real users depend on.
How to personalize
Replace these placeholders before posting:
- {{company}}
- {{location}}
- {{work_type}}
- {{salary_range}}
- {{hiring_manager}}
The bracketed notes — like [your benefits] or [your primary language(s)] — are prompts to swap in your own details. The more specific you are about the actual work and stack, the stronger your applicant pool will be.
Frequently asked questions
- What does a Machine Learning Engineer do?
- A Machine Learning Engineer takes machine learning models into production and keeps them running. They build training and inference pipelines, serving infrastructure, and monitoring — turning a promising model into a reliable, scalable product feature.
- What's the difference between a Machine Learning Engineer and a Data Scientist?
- A Data Scientist builds and validates models and focuses on insight and experimentation. A Machine Learning Engineer focuses on the engineering required to deploy and operate those models reliably at scale. The two work closely together, often handing off from prototype to production.
- What skills should a Machine Learning Engineer have?
- Strong software engineering fundamentals, proficiency in Python, experience deploying and operating models in production, and familiarity with ML frameworks like PyTorch or TensorFlow. A pragmatic focus on reliability, latency, and cost matters more than novel modeling.