[Remote] Machine Learning Engineer, Positioning

Note: The job is a remote job and is open to candidates in USA. Lime is the largest global shared micromobility business, operating in close to 30 countries across five continents. As a Machine Learning Engineer, you’ll help design, build, and scale the ML systems that power demand forecasting and vehicle positioning, collaborating closely with data scientists and engineers to enhance the rider experience.

Responsibilities

• 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

Skills

• 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
• 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

Benefits

• Discretionary annual performance bonus opportunities
• Equity

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