Senior Machine Learning Engineer Job Description Template

The senior machine learning engineer takes models from research into production and keeps them working there. The role covers the full path from data and training through to serving, monitoring and retraining, and it carries responsibility for the parts that decide whether a model is actually useful: latency, cost, reliability and the detection of silent degradation. Most of the difficulty in applied machine learning lives outside the model itself.

Typical Duties and Responsibilities

• Design, train and evaluate models against clearly defined objectives
• Build training pipelines that are reproducible and auditable
• Deploy models to production with appropriate latency and cost characteristics
• Build monitoring for model performance, drift and data quality
• Design offline and online evaluation, including A/B testing
• Optimize inference cost and serving performance
• Investigate model failures and trace them to data or training causes
• Mentor engineers on machine learning practice
• Work with product on what a model can and cannot reasonably do
• Maintain documentation of data sources, training and known limitations

Education

• Bachelor’s or Master’s degree in computer science, machine learning, statistics or a related field

Required Skills and Experience

• 5+ years of machine learning engineering with models in production
• Strong Python and a modern framework such as PyTorch or TensorFlow
• Solid software engineering practice including testing and version control
• Experience with training pipelines and experiment tracking
• Understanding of evaluation design and its common failures
• Experience monitoring deployed models and responding to drift
• Ability to reason about inference cost and latency trade offs
• Clear communication about model uncertainty

Preferred Qualifications

• Experience with distributed training
• Exposure to feature stores or a managed ML platform