Staff Machine Learning Engineer, Supply

Own the technical vision and roadmap for ML systems powering forecasting, supply positioning, and fleet optimization.

Lead end-to-end execution of complex, cross-functional ML initiatives, from problem framing through production impact, ensuring alignment with business goals.

Act as the primary technical decision-maker for the team, setting architecture, modeling approaches, and engineering standards.

Mentor and develop engineers, providing technical guidance and raising the overall bar for ML and software engineering excellence.

Partner with product and operations leadership to shape strategy, prioritize investments, and ensure ML solutions drive measurable outcomes.

Establish best practices for ML development, deployment, monitoring, and iteration at scale.

Identify and drive high-leverage opportunities, balancing short-term impact with long-term platform and modeling investments.

Serve as a hands-on technical leader, contributing to critical parts of the codebase while enabling others to execute effectively.

Write clean, maintainable, well-tested code by thoughtfully leveraging AI-assisted development workflows to improve quality and efficiency while maintaining high standards for validation, security, and ownership.

7+ years of experience in software engineering and machine learning, with a track record of leading large, complex ML systems in production.

Demonstrated experience acting as a technical lead or de facto team lead, driving projects across multiple engineers and stakeholders.

Strong system design skills, including architecture of scalable ML systems, data pipelines, and real-time or batch inference systems.

Proven ability to translate ambiguous business problems into clear technical strategies and deliver measurable impact.

Experience mentoring and developing engineers, with a track record of raising team performance and influencing engineering culture.

Strong coding skills in Python and experience with modern ML frameworks (e.g., PyTorch, TensorFlow) and data tools (SQL, Spark).

Experience using AI-assisted development tools to support coding, debugging, testing, documentation, technical exploration, and analysis.

Ability to validate AI-assisted outputs, identifying risks and failure modes, protecting sensitive information, and knowing when deeper manual review is required.

Preferred Experience:

Experience owning or leading ML platforms or systems at company or org-wide scale.

Background in time-series modeling, forecasting, optimization, or operations research applied to real-world systems.

Familiarity with experimentation frameworks, causal inference, and decision-making under uncertainty.