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Job market data

The senior ML engineer market is concentrated across the San Francisco Bay Area, Seattle, New York, and Boston, with meaningful pockets in Austin and Denver. Mid-senior compensation generally lands in a $150K–$230K base band before equity, and director-level roles regularly break $300K total. What distinguishes the strongest senior MLEs we see today is the range — they can move fluently between classical supervised learning, deep learning, and the LLM stack, and they own production reliability as much as model performance.

Key responsibilities

Lead the design and delivery of production ML systems at scale, owning them from research through long-term operation
Mentor mid-level ML engineers and set the technical bar for model development, review, and production practices
Own end-to-end ML platform decisions — frameworks, training infrastructure, model serving, feature stores, and evaluation
Drive cross-team alignment on technical approach for complex, multi-model systems
Partner closely with research and applied science teams to productionize novel approaches without sacrificing reliability
Establish MLOps standards for monitoring, cost tracking, release practices, and incident response
Contribute to hiring — interviews, calibration, and assessment rubric design
Represent the ML engineering function in cross-functional planning and senior technical forums

Candidate requirements

6+ years of professional ML engineering experience with real production ownership
Deep expertise in PyTorch or TensorFlow, transformer architectures, and modern fine-tuning techniques
Strong systems engineering background — distributed training, efficient inference, and cost-aware design
Track record of owning end-to-end ML systems from research through long-term operation
Experience leading technical design and mentoring engineers
Excellent written and verbal communication

Senior Machine Learning Engineer