Machine Learning Engineer job description template
A machine learning engineer working in vision builds the systems around models — training pipelines, evaluation infrastructure, deployment and monitoring — as much as the models themselves. The emphasis is on making machine learning work repeatably rather than on novel methods.
Before you post
State plainly what proportion of the job is modelling versus infrastructure. This is the single biggest source of mis-hire in this title. Give a sense of scale too — request volume, data size, number of models — because that determines whether the role is interesting to infrastructure-minded candidates.
The template
About the role
We are looking for a machine learning engineer to build the infrastructure our vision models run on — training, evaluation, deployment and monitoring — so the team can ship model improvements quickly and safely.
What you will do
- Build and maintain training and evaluation pipelines
- Deploy models to production and own their serving performance
- Instrument monitoring for accuracy regression and distribution shift
- Improve inference cost and latency across [scale / workload]
- Establish reproducibility and experiment tracking practice
What we are looking for
- Strong Python and production software engineering practice
- Experience deploying machine learning models to production
- Containerisation and cloud infrastructure experience
- Comfort with model evaluation and monitoring
Keep this list to three to five items. Long mandatory lists disproportionately deter the candidates you most want.
Nice to have
- Computer vision or video pipeline experience
- Inference optimisation with TensorRT, ONNX Runtime or similar
- Kubernetes and GPU scheduling
- Experience with MLflow, Weights & Biases or equivalent
Plain text version
Select all and paste into your ATS, then replace everything in brackets.
MACHINE LEARNING ENGINEER ABOUT THE ROLE We are looking for a machine learning engineer to build the infrastructure our vision models run on — training, evaluation, deployment and monitoring — so the team can ship model improvements quickly and safely. WHAT YOU WILL DO - Build and maintain training and evaluation pipelines - Deploy models to production and own their serving performance - Instrument monitoring for accuracy regression and distribution shift - Improve inference cost and latency across [scale / workload] - Establish reproducibility and experiment tracking practice WHAT WE ARE LOOKING FOR - Strong Python and production software engineering practice - Experience deploying machine learning models to production - Containerisation and cloud infrastructure experience - Comfort with model evaluation and monitoring NICE TO HAVE - Computer vision or video pipeline experience - Inference optimisation with TensorRT, ONNX Runtime or similar - Kubernetes and GPU scheduling - Experience with MLflow, Weights & Biases or equivalent ABOUT US [Two or three sentences on the company, the product, and why the problem matters.] DETAILS - Location: [city / hybrid / remote] - Salary: [range] - Apply: [link]
Calibrating the level
Adjust the requirements to match the level you are actually hiring for. Asking for senior capability at a mid-level budget is the most common cause of a stalled search.
Junior
Builds and maintains training pipelines and deployment plumbing against a defined spec.
Mid
Owns the path from experiment to production for a model family, including monitoring.
Senior
Designs the ML platform other engineers build on and sets evaluation standards.
Staff / Principal
Owns ML system architecture across teams and the trade-offs between model quality, cost and reliability.
Post it where the right people are
JobsInVision is a computer vision job board — your listing reaches engineers who work in this field specifically, rather than a general software audience.