About the Machine Learning Engineer role
A Machine Learning Engineer takes models from idea to production. They train, evaluate, and deploy machine learning systems, build the pipelines that feed them data, and monitor their behavior once real users depend on them. The role combines applied modeling with solid software engineering.
Unlike a research-focused data scientist, an ML engineer is judged on what runs reliably in production. A strong candidate writes maintainable code, understands serving latency and cost, versions data and models properly, and knows when a simple baseline beats a complex architecture. Many ML engineers also work with large language models, building retrieval pipelines, evaluation harnesses, and fine-tuning workflows.
In your posting, describe the problems the models solve, the maturity of your ML infrastructure, and the frameworks you use. State whether the role leans toward research, production engineering, or LLM applications. Candidates self-select heavily based on where the role sits on that spectrum.
Machine Learning Engineer job description template
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Job brief
We are hiring a Machine Learning Engineer to take models the whole way. You will train and evaluate them, build the pipelines that feed them, deploy them where real users depend on them, and watch what happens once the traffic is not the traffic you tested on.
This suits somebody who writes maintainable code, thinks about latency and cost as part of the design, and is willing to ship a simple baseline when the simple baseline is the right answer.
Responsibilities
- Design, train, and evaluate machine learning models for production use cases
- Build and maintain data preprocessing and feature pipelines for training and inference
- Deploy models to production and own serving performance, latency, and cost
- Monitor models in production and detect drift, degradation, and data issues
- Establish experiment tracking, model versioning, and reproducible training workflows
- Run offline and online evaluations, including A/B tests, to validate model impact
- Collaborate with product managers and engineers to frame problems and define success metrics
- Write clean, tested, well-documented code and review the work of peers
- Stay current with ML research and assess which advances are worth adopting
Requirements and skills
- Has taken machine learning systems into production and kept them there
- Strong in Python, writing code other people can maintain
- Hands-on with a framework such as PyTorch, TensorFlow, or scikit-learn
- Grounded in statistics and evaluation, including how models quietly fail
- Has deployed and served models in a real environment
- Comfortable with a cloud platform and with containers
- Works confidently in SQL and with datasets that do not fit in memory
- A relevant degree, or the practical experience that replaced one
Nice to have
- Experience with LLM applications, including retrieval-augmented generation and fine-tuning
- Familiarity with MLOps tooling such as MLflow, Kubeflow, or SageMaker
- Experience with Kubernetes and scalable inference infrastructure
- Published research or open source contributions in machine learning
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