About the Role
Develop scalable systems that power machine learning workflows for robotics, including training, deployment, and monitoring in production environments.
Responsibilities
- Design and implement core infrastructure for training and deploying machine learning models
- Optimize data pipelines to handle high-volume sensor inputs from robotic fleets
- Collaborate with research and robotics teams to integrate ML systems into real-world operations
- Improve model monitoring, versioning, and reproducibility across experiments
- Build tools to streamline the ML development lifecycle
- Ensure reliability and performance of ML systems in dynamic environments
- Work on distributed computing frameworks to support large-scale training jobs
- Maintain and scale model serving infrastructure
- Develop testing frameworks for ML components
- Support deployment of models across edge devices and cloud environments
- Troubleshoot issues in production ML pipelines
- Contribute to documentation and best practices for ML engineering
- Evaluate new tools and technologies for ML infrastructure
- Ensure data consistency and integrity across training and inference
- Help define architecture for long-term scalability of ML systems
Nice to Have
- Master’s degree in a technical field related to machine learning or robotics
- Experience with autonomous systems or robotics software stacks
- Contributions to open-source ML infrastructure projects
- Hands-on experience with real-time inference systems
- Background in systems programming or performance optimization
- Exposure to safety-critical or regulated environments
- Prior work in fast-paced startup environments
Compensation
Competitive salary with equity and benefits
Work Arrangement
Hybrid
Team
Small, cross-functional team focused on autonomous robotics systems
What We Do
- Develop self-driving delivery robots that operate on sidewalks in dense urban areas
- Deploy autonomous vehicles in partnership with major logistics and retail companies
- Focus on sustainable, zero-emission last-mile delivery solutions
Our Environment
- Fast-moving, hands-on engineering culture
- Emphasis on rapid prototyping and real-world testing
- Close collaboration between software, hardware, and operations teams
Available for qualified candidates
