Staff AI Engineer

Heads up: this posting is for future opportunities rather than one specific open role. If you apply, we’ll add you to our candidate network and may reach out when relevant roles come up.

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What the market looks like

We've tracked 34,500+ mid-level AI/ML engineering postings in the US over the last six months, with strong hiring concentration in California, New York, and Texas across technology, financial services, and professional services firms. Median compensation for this cohort lands around $180,000, with top performers commanding packages that reach $350,000 as companies compete for depth in model development, production ML systems, and AI infrastructure. The strongest candidates in this space combine solid software engineering fundamentals with hands-on experience shipping ML models to production—they understand the gap between research-grade code and systems that scale, and they ship with velocity.

Job responsibilities

• Design, develop, and optimize machine learning models and pipelines that power production systems, from experiment through deployment and monitoring.

• Lead the full lifecycle of AI/ML features: requirements gathering, architecture design, implementation, testing, and iterative improvement based on real-world performance.

• Partner with data engineers, platform teams, and product stakeholders to integrate ML systems into larger applications and ensure reliability at scale.

• Own code quality, testing strategy, and documentation practices; mentor junior engineers on ML development best practices.

• Troubleshoot model performance in production, identify root causes of degradation, and implement fixes or retraining pipelines as needed.

• Drive technical decisions around frameworks, libraries, and infrastructure; evaluate trade-offs between accuracy, latency, cost, and maintainability.

• Collaborate on data strategy: assess data quality, design feature engineering approaches, and build reproducible data pipelines.

Candidate requirements

• 5–8 years of software engineering or ML engineering experience, with at least 2–3 years shipping ML systems to production (not research or one-off analysis).

• Strong command of Python and familiarity with common ML frameworks (PyTorch, TensorFlow, scikit-learn, or equivalent) and deployment tools (Docker, Kubernetes, cloud platforms).

• Demonstrated ability to take a model from prototype to production: you understand data pipelines, model serving, monitoring, and the operational side of ML systems.

• Solid grasp of software engineering fundamentals—version control, testing, CI/CD, code review—and how they apply to ML codebases.

• Track record of working cross-functionally with data scientists, product, and infrastructure teams; you can translate between research and engineering.

• Comfort with ambiguity and rapid iteration; you've built features in domains where best practices are still evolving and you thrive in that environment.