Senior Machine Learning Engineer

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What the market looks like

We've tracked 14,100+ specialist-level machine learning engineering postings across the United States in the last six months, with hiring concentrated in technology hubs like San Francisco, Seattle, and New York. Compensation for this cohort typically ranges from $200,000 to $360,000 annually. The strongest candidates combine deep hands-on experience shipping ML systems with the ability to navigate tradeoffs between research rigor and production constraints. They excel at moving models from prototype to deployment, debugging production ML failures, and collaborating across data science, infrastructure, and product teams to ensure systems scale and perform reliably.

Job responsibilities

• Design, build, and own end-to-end machine learning pipelines that move from experimentation to production deployment, handling data preprocessing, model training, evaluation, and inference at scale

• Debug and troubleshoot production ML systems, identifying performance regressions, data drift, and latency issues, and implement solutions to improve model reliability and speed

• Partner with data scientists to translate research and prototypes into robust, maintainable production code, balancing model accuracy with engineering pragmatism

• Collaborate with infrastructure and MLOps teams to integrate ML systems into platform architecture, manage dependencies, and optimize compute and storage efficiency

• Drive experimentation and iteration on model architectures, training approaches, and feature engineering, using metrics and A/B testing to validate improvements

• Mentor junior engineers on ML engineering best practices, code quality standards, and system design for production ML applications

• Own technical documentation and knowledge transfer for model pipelines, ensuring teams can maintain and extend systems over time

Candidate requirements

• 5+ years of hands-on experience building and shipping machine learning systems in production environments, with proven ability to move models from research to deployment

• Strong proficiency in Python and a deep understanding of ML frameworks (TensorFlow, PyTorch, scikit-learn) and data manipulation libraries (pandas, NumPy)

• Solid grasp of ML fundamentals including model selection, feature engineering, evaluation metrics, cross-validation, and debugging ML-specific failure modes

• Experience with ML infrastructure and tooling—data pipelines, experiment tracking, model serving, and containerization (Docker, Kubernetes)—or willingness to learn rapidly

• Track record of collaborating effectively with cross-functional teams (data scientists, product, infrastructure) and communicating technical tradeoffs to non-technical stakeholders

• Comfort working in remote or distributed environments with asynchronous communication and a bias toward clear documentation