Staff Machine Learning Engineer, Supply

Lime; Canada; On-site; CAD 172,000 - 237,000; Full time

Benefits offered by this job

Offers Equity

Offers Bonus

Job summary

Lime is on a mission to build a future where transportation is shared, affordable, and carbon-free. They are looking for a Staff Machine Learning Engineer to develop and lead ML-powered Supply & Fleet Optimization Systems.

The ideal candidate will have 7+ years of experience in software engineering and machine learning, excellent coding skills in Python, and be responsible for shaping the technical direction of ML initiatives. This is a remote position for candidates residing in Canada.

Qualifications

• 7+ years of experience in software engineering and machine learning.
• Experience acting as a technical lead or team lead.
• Proven ability to translate ambiguous business problems into clear technical strategies.

Responsibilities

• Own the technical vision and roadmap for ML systems.
• Lead end-to-end execution of complex ML initiatives.
• Act as the primary technical decision-maker for the team.
• Mentor and develop engineers.
• Establish best practices for ML development and deployment.

Skills

Software engineering and machine learning experience

Technical leadership

System design skills

Strong coding skills in Python

Experience with ML frameworks

Tools

PyTorch

TensorFlow

SQL

Spark

Job description

Location

Canada

Employment Type

Full time

Location Type

Remote

Department

Engineering

Compensation

• CA$172K – CA$237K • Offers Equity • Offers Bonus

As a global leader in micromobility, 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.

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 Canada to maintain effective collaboration across teams.

What You’ll Do:

• 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.

About You:

• 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).

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.

The base salary range listed reflects what Lime reasonably expects to offer for this role, with the final base salary determined by objective factors such as the candidate’s location and relevant skills and experience. Depending on the position, the total compensation package may also include discretionary annual performance bonus opportunities and equity, subject to applicable plan terms and eligibility requirements.

Lime is proud to be an Equal Opportunity Employer. We believe different perspectives help us grow and achieve more. That’s why we’re dedicated to building and developing a team that reflects a wider range of backgrounds, abilities, identities, and experiences. If you require a reasonable accommodation during the application or hiring process, please email [e-mail] for assistance.