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
Job brief
We are looking for a Machine Learning Engineer to design, build, and deploy machine learning systems that deliver measurable business value. You will own the full model lifecycle: framing problems, preparing data, training and evaluating models, shipping them to production, and monitoring their performance over time. To succeed in this role, you should combine strong ML fundamentals with the software engineering skills to run models reliably at scale.
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
- Proven working experience as a Machine Learning Engineer or in a similar applied ML role
- Strong programming skills in Python and experience writing production-quality code
- Hands-on experience with ML frameworks such as PyTorch, TensorFlow, or scikit-learn
- Solid grounding in statistics, model evaluation, and common failure modes such as overfitting and leakage
- Experience deploying and serving models in production environments
- Familiarity with cloud platforms such as AWS, GCP, or Azure and with containerization tools such as Docker
- Experience with SQL and working with large datasets
- MSc or BSc in Computer Science, Machine Learning, or a related field, or equivalent experience
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
Generate a custom Machine Learning Engineer job description
Need a version tailored to your company, seniority level, or industry? Describe the role and our AI job description generator will draft one in seconds.
