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What the market looks like

We've tracked 34,500+ mid-level machine learning engineering postings across the US in the last six months, with concentrated hiring in California, New York, and Texas. The market spans technology, financial services, healthcare, and manufacturing — each with distinct ML infrastructure and model-deployment challenges. Median compensation lands around $180,000, with top-tier packages reaching $350,000+. The strongest candidates bring 5+ years of hands-on experience shipping production ML systems: they're fluent in model training and optimization, comfortable owning data pipelines and infrastructure, and skilled at partnering with data scientists and backend teams to move prototypes into scaled deployments.

Typical job responsibilities

Design, build, and ship machine learning models and inference systems in production environments, owning quality, latency, and scalability
Lead architecture decisions around feature stores, training pipelines, model serving, and monitoring — balancing accuracy, cost, and operational simplicity
Partner with data scientists and product teams to translate research into deployed systems, defining success metrics and managing technical trade-offs
Drive MLOps improvements: build tooling for data versioning, experiment tracking, model registry, and continuous deployment workflows
Troubleshoot production ML systems — debugging model performance issues, retraining strategies, and drift detection
Contribute to platform and infrastructure decisions that scale ML capabilities across the organization
Mentor junior engineers and participate in code review and technical design discussions

Typical candidate requirements

5+ years building and deploying machine learning systems in production — not just experimentation or academia
Strong software engineering fundamentals: API design, testing, version control, and deployment pipelines
Hands-on experience with model training frameworks (PyTorch, TensorFlow) and MLOps tools — you've written real training and inference code
Demonstrable experience with data engineering: SQL, distributed data processing, or feature pipeline work
Comfortable communicating technical tradeoffs to non-ML stakeholders and working across teams
Experience shipping at least one ML system at scale — defining success metrics, monitoring in production, and iterating based on real-world behavior

Sr. Machine Learning Engineer