About the Role
The candidate will develop scalable software systems that support machine learning workflows, from data ingestion to model deployment, and help bridge infrastructure across robotics and cloud environments.
Responsibilities
- Design and implement backend services for machine learning pipelines
- Build APIs that connect robotic systems with cloud infrastructure
- Develop tools for monitoring model performance in production
- Create data processing workflows for training and evaluation
- Support deployment of machine learning models on edge devices
- Collaborate with robotics engineers to integrate perception systems
- Optimize data storage and retrieval for high-frequency sensor data
- Improve reliability of distributed training infrastructure
- Write clean, maintainable code with comprehensive testing
- Troubleshoot and debug system-level issues across services
- Contribute to frontend applications for data labeling and analysis
- Ensure secure handling of sensitive operational data
- Automate repetitive tasks in the development and deployment cycle
- Work on versioning systems for models and datasets
- Support continuous integration and delivery pipelines
- Collaborate with research teams to productionize experimental models
- Instrument systems for observability and logging
- Scale infrastructure to support growing fleet operations
- Maintain documentation for internal tools and APIs
- Evaluate new technologies for improving system performance
Nice to Have
- Master’s degree in Computer Science or related field
- Experience with edge computing in robotics
- Contributions to open-source machine learning projects
- Familiarity with real-time data streaming platforms
- Experience with TensorFlow or PyTorch in production
- Knowledge of distributed systems design
- Exposure to safety-critical software environments
- Understanding of regulatory compliance for data handling
Compensation
Competitive salary and equity package
Work Arrangement
Hybrid
Team
Small, cross-functional team focused on robotics and machine learning systems
What We Value
- Practical problem-solving over theoretical perfection
- Ownership of projects from design to deployment
- Clear communication across technical and non-technical roles
- Willingness to work on both high-level architecture and low-level details
Tech Stack
- Primary languages: Python, TypeScript
- Cloud: AWS, Kubernetes
- Databases: PostgreSQL, Redis, MongoDB
- ML tools: TensorFlow, PyTorch, MLflow
- Frontend: React, Next.js
- Infrastructure: Terraform, Docker, Jenkins
Available for qualified candidates
