Machine Learning Engineer, Positioning

Lime is the largest global shared micromobility business, operating in close to 30 countries across five continents. We’re on a mission to build a future where transportation is shared, affordable and carbon-free. Our electric bikes and scooters have powered more than one billion rides in cities around the world. Named a 2025 Time 100 Most Influential Company, Lime continues to set the pace for shared micromobility globally, spurring a new generation of clean alternatives to car ownership.

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 Machine Learning team is central to this mission, building demand forecasts, recommending deployment strategies, and creating models that directly influence millions of rides worldwide.

As a Machine Learning Engineer, you’ll help design, build, and scale the ML systems that power these decisions. You’ll work closely with data scientists, backend engineers, and operations teams to turn data-driven insights into production-ready models that improve our rider experience every day.

What You’ll Do:

• Develop, test, and deploy ML models and optimization services that improve Lime’s demand forecasting and vehicle positioning capabilities.

• Collaborate with data scientists to productionize research models and ensure scalability, reliability, and performance.

• Build and maintain data pipelines and feature engineering workflows that feed Lime’s ML systems.

• Contribute to the design and implementation of new ML-driven features, working with product and operations partners to deliver measurable business value.

• Monitor and iterate on deployed models to maintain accuracy and adapt to real-world changes.

• Participate in technical discussions, share learnings, and contribute to improving ML engineering best practices across the team.

About You:

• 1–3 years of professional experience in software engineering or applied ML, with a record of delivering production-quality systems.

• Proficiency in Python and experience with modern ML frameworks (e.g., PyTorch, TensorFlow) and data tools (SQL, Pandas, Spark, Airflow).

• Experience training and evaluating models on real-world data, including managing model performance over time.

• Solid understanding of ML system fundamentals such as data preprocessing, feature extraction, and model monitoring.

• Strong problem-solving skills and ability to work effectively in a cross-functional environment.

Preferred Experience:

• Experience with time-series or forecasting models.

• Familiarity with optimization or operations research concepts, especially for logistics or resource allocation problems.

• Knowledge of A/B testing, causal inference, or other experimentation techniques.

• Exposure to geospatial or spatiotemporal data in applied ML contexts.

• Proficiency in MLOps, such as shadow models, feature/model stores, and versioning.

The salary range listed reflects what Lime reasonably expects to offer for this role, with the final base salary determined by 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.

#LI-PP1

#LI-Remote

If you want to make an impact, Lime is the place for you. Not sure if you meet all the qualifications? If this role excites you we encourage you to apply. Explore all opportunities on our career page.

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 [email protected] for assistance.