Senior Machine Learning Engineer

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What the market looks like

We've tracked 14,900+ specialist-level machine learning engineering postings in the last six months across the US, with the strongest hiring concentrated in California, Washington, and New York. The market spans technology, financial services, healthcare, and manufacturing — organizations across sectors are moving ML from prototype to production and need engineers who can ship. Compensation for this cohort typically lands between $200k and $360k annually. The strongest candidates bring hands-on experience deploying models at scale, fluency with modern ML infrastructure and tooling, and the ability to collaborate across data science, platform, and product teams to turn research into real systems.

Job responsibilities

• Design, build, and maintain production ML systems and pipelines that serve business-critical applications at scale

• Own the full lifecycle of model deployment: from data preprocessing and feature engineering through model evaluation, versioning, monitoring, and retraining

• Partner with data scientists and research teams to translate algorithms and experiments into robust, performant production code

• Drive infrastructure and tooling decisions around model serving, feature stores, experiment tracking, and ML observability

• Lead performance optimization efforts — reducing latency, improving throughput, and managing costs in ML systems

• Collaborate with platform, product, and analytics teams to integrate ML capabilities into broader business applications

• Contribute to ML standards, best practices, and documentation that help the wider engineering organization build and maintain ML systems

Candidate requirements

• 4+ years of hands-on machine learning engineering experience, with demonstrated success shipping ML systems to production

• Strong foundation in ML fundamentals: model training, evaluation, feature engineering, and common pitfalls in moving models to production

• Fluency in Python and experience with modern ML frameworks (PyTorch, TensorFlow, scikit-learn) and MLOps tooling (experiment tracking, model registries, orchestration platforms)

• Proven ability to design and optimize data pipelines and ML infrastructure for reliability, scalability, and maintainability

• Comfortable working cross-functionally with data scientists, engineers, and product teams — you can translate between research ideas and production constraints

• Experience building systems that need to run reliably in production: monitoring, debugging, incident response, and iterative improvements