Principal Machine Learning Engineer

Lime is the largest global shared micromobility business, operating in close to 30 countries across five continents. They are seeking a Principal Machine Learning Engineer to join the Data Science & Analytics team, responsible for shaping how machine learning is built, deployed, and operated across Lime.

Responsibilities:

• Drive alignment across teams on ML strategy, standards, and long-term technical direction by serving as a technical leader for Lime’s ML Center of Excellence
• Guide recommendations for ML infrastructure, tooling, and architecture (training, serving, feature stores, experimentation, monitoring)
• Define and evolve ML development processes, including model review, experimentation rigor, deployment, optimization, and operations
• Establish best practices for ML monitoring, observability, alerting, and model performance health in production
• Drive reusable feature development patterns and shared ML capabilities that enable teams to move faster and more safely
• Partner with platform, data, and product engineering teams to ensure ML systems are reliable, scalable, and cost effective
• Identify and prioritize opportunities where ML will improve Lime’s product, operations, or efficiency
• Act as a force multiplier by mentoring data scientists and machine learning engineers, raising the quality bar for machine learning across Lime

Requirements:

• 8+ years of professional experience in software engineering or applied ML, with a record of delivering production level systems
• Fluency in Python and experience with modern ML frameworks (e.g., PyTorch, TensorFlow) and data tools (e.g. SQL, pandas, spark, airflow)
• Strong foundation in ML fundamentals, including model evaluation, experimentation, optimization, production deployment, and operations
• Strong system design skills and comfort working with distributed systems
• Track record of influencing ML architecture and practices across multiple teams
• Background in domains relevant to Lime (e.g., forecasting, optimization, pricing, marketplace dynamics)
• Prior experience building a ML platform or center of excellence through defining ML standards, governance, or shared tooling at scale