Senior AI Engineer

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What the market looks like

We've tracked over 44,300 specialist-level AI engineering postings in the US over the last six months, with strongest hiring concentrated in California, New York, and Texas across professional services, technology, and financial services. Compensation for specialist-level AI engineers ranges from $170,000 to $350,000 annually. The strongest candidates bring hands-on experience shipping production AI systems—not just experimentation—along with fluency in modern ML frameworks, infrastructure tooling, and the ability to translate between research concepts and deployed models that solve real business problems.

Job responsibilities

• Design, build, and deploy machine learning models and systems that solve measurable business problems at scale

• Own the full ML lifecycle: data pipeline design, model training and validation, optimization, and production inference

• Partner with product and data teams to define AI requirements, translate user needs into technical specifications, and integrate models into production applications

• Build and maintain ML infrastructure, monitoring, and testing practices that ensure model reliability and performance in production

• Troubleshoot model performance issues, conduct experiments to improve accuracy and latency, and document lessons learned

• Evaluate emerging AI tools, frameworks, and techniques; advocate for adoption where they measurably improve engineering velocity or model quality

• Mentor junior engineers and contribute to technical documentation and knowledge sharing across the engineering organization

Candidate requirements

• 5+ years of hands-on experience building and shipping machine learning systems in production environments

• Deep fluency with at least one modern ML framework (PyTorch, TensorFlow, JAX) and production ML infrastructure (model serving, experiment tracking, data pipelines)

• Demonstrated ability to move from problem definition through model deployment—comfort with both research and engineering disciplines

• Strong fundamentals in software engineering: version control, testing, debugging, and code review practices

• Experience collaborating with cross-functional teams (product, data, operations) and communicating technical constraints and tradeoffs clearly

• Self-directed learner with curiosity about new tools and techniques in AI/ML, balanced by pragmatism about what matters for the business