Principal Applied Scientist - Computer Vision

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.

We are looking for a Principal Applied Scientist to serve as the technical architect and strategic lead for advanced computer vision and machine learning systems. In this role, you will define how intelligent, real-world systems perceive and interpret their environment, from concept through large-scale production deployment.

You will work on complex applied perception challenges that require a strong blend of computer vision, machine learning, and data expertise, with solutions operating in real time on edge hardware. This role requires a hands‑on technical leader who can set long-term technical direction while balancing system performance, reliability, and user experience in dynamic, real‑world conditions.

This is a remote position with a requirement for candidates residing in Canada to maintain effective collaboration across teams.

What you’ll do:

Technical Strategy &

Roadmap:

Define and execute the long-term technical vision for applied computer vision systems, identifying high-impact opportunities and guiding architectural decisions.

Complex Problem Solving:
Architect and own real-time perception and scene-understanding solutions designed to operate reliably in complex, real-world environments.

Advanced Data Fusion:
Lead efforts to combine computer vision with additional sensor inputs to improve robustness, accuracy, and contextual understanding.

Hardware–Software Co-Design:
Collaborate with hardware and firmware teams to optimize vision algorithms for deployment on resource-constrained edge devices, ensuring low latency and high reliability across diverse conditions.

End-to-End Ownership:
Oversee the full lifecycle of machine learning solutions—from data strategy and model development to deployment, monitoring and iterative improvement in production.

Technical Leadership & Mentorship:
Act as a senior technical authority for computer vision and applied ML; mentor scientists and engineers and promote a culture of rigorous, production-oriented development.

Cross-Functional Influence:
Partner with Product, Engineering, and other stakeholders to translate high-level requirements into scalable, technically sound solutions.

Strategic

Innovation: Stay current with advances in AI and computer vision research, identifying techniques that can be adapted for practical, real-world applications.

About you:

8+ years of industry experience in Computer Vision and Machine Learning, with a strong track record of deploying production‑grade systems in real-world environments.

Edge AI Expertise:
Extensive experience optimizing and deploying CV/ML models on embedded or edge hardware platforms where power, performance, and efficiency constraints are critical.

Sensor Fusion

Experience:

Deep understanding of integrating with additional sensor modalities to solve complex perception or state‑estimation problems.

Strategic Technical Leadership:
Demonstrated experience leading technical strategy and influencing architecture across teams; people management experience is a plus.

Real-World Problem Solving:
Proven ability to build robust systems that perform reliably under noisy, variable, and imperfect real-world conditions.

Academic Foundation:
Master’s or PhD in Computer Science, Robotics, Electrical Engineering, or a related field with a focus on Computer Vision or Machine Learning.

Annual performance bonus, equity and benefits may be provided as part of the compensation package, depending on the position offered. Not all candidates will be eligible for the upper end of the salary range. Exact salary will ultimately depend on multiple factors, which may include the candidate’s location of residence, the successful candidate's skills, experience and…