Staff ML Engineer, Supply & Fleet Optimization (Remote)

LimeBike Germany GmbH; Northern (KY); Hybrid; USD 150,000 - 240,000; Full time

Benefits offered by this job

Comprehensive Health & Wellness

401(k) plan

Parental leave

Unlimited vacation

Lime vehicle perks

Job summary

Lime is seeking a Staff Machine Learning Engineer to lead ML systems for forecasting, supply positioning, and fleet optimization. You will own the technical vision, drive cross-functional ML projects, and set architecture and standards while mentoring engineers across the team.

You will collaborate with product and operations to translate business problems into scalable ML solutions, ensuring high impact and robust deployment practices across our remote work environment in the United States.

Qualifications

• 7+ years of experience in software engineering and machine learning.
• Strong system design skills for scalable ML systems.
• Proven ability to translate ambiguous problems into technical strategy.
• Experience mentoring and developing engineers.

Responsibilities

• Own the technical vision and roadmap for ML systems powering forecasting, supply positioning, and fleet optimization.
• Lead end-to-end execution of cross-functional ML initiatives from problem framing to production impact.
• Define architecture, modeling approaches, and engineering standards for ML platforms.
• Mentor engineers and raise team performance.
• Partner with product and operations to prioritize investments and ensure measurable outcomes.

Skills

Python

Machine learning

Leadership

Tools

PyTorch

TensorFlow

SQL

Spark

Job description

Lime is seeking a Staff Machine Learning Engineer to lead ML systems for forecasting, supply positioning, and fleet optimization. You will own the technical vision, drive cross-functional ML projects, and set architecture and standards while mentoring engineers across the team.

You will collaborate with product and operations to translate business problems into scalable ML solutions, ensuring high impact and robust deployment practices across our remote work environment in the United States.