Staff Machine Learning Engineer, Supply

As the largest global shared micromobility business, Lime is on a mission to build a future where transportation is shared, affordable and carbon-free. A Time Magazine 100 Most Influential Company, Lime has powered more than one billion rides in close to 30 countries across five continents, spurring a new generation of clean alternatives to car ownership. Learn more at li.me.

At Lime, our mission is to ensure a scooter or bike is ready for you at the right place and the right time. Achieving this requires solving one of the most complex optimization problems in mobility: how to deploy and continually rebalance vehicles across a dynamic, ever-changing city. The Supply Machine Learning team is central to this mission, building forecasts, recommending deployment strategies, and creating models that directly influence millions of rides worldwide.

As a Staff Machine Learning Engineer, you will serve as the technical leader for Lime’s ML-powered Supply & Fleet Optimization Systems. You will define the long-term technical direction, lead execution across multiple ML initiatives, and ensure we are building scalable, high-impact systems that drive business outcomes. This role combines deep technical expertise with strong ownership and mentorship, acting as a force multiplier for the team. We are looking for engineers who thoughtfully integrate modern AI tools into their work: using them to raise quality, solve problems and move faster while maintaining strong engineering judgment and ownership.

You’ll partner closely with product, operations, and engineering leadership to translate ambiguous, high-stakes problems into clear strategies and executable plans, while guiding a team of engineers to deliver at a high bar.

This is a remote position with a requirement for candidates to reside in the United States to maintain effective collaboration across teams.

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

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

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

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

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

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

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

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

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  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.