Staff Machine Learning Engineer, Supply

Lime

40d•$200,000 - $275,000•Remote

About The Position

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.

Requirements

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.

Nice To Haves

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.

Responsibilities

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.