About the Role
Design, train, and deploy machine learning models that process camera inputs to enable accurate environmental understanding in self-driving systems.
Responsibilities
- Develop deep learning algorithms tailored to camera-based perception tasks
- Optimize neural networks for real-time inference on embedded platforms
- Collaborate with sensor and software teams to integrate camera models into broader systems
- Improve model accuracy using large-scale, diverse driving datasets
- Implement data augmentation and synthetic data strategies to enhance training robustness
- Diagnose and resolve performance issues in deployed vision models
- Contribute to the design of camera calibration and validation pipelines
- Work with 3D scene reconstruction and depth estimation from monocular and stereo inputs
- Support labeling infrastructure improvements for camera data annotation
- Evaluate model performance across varied lighting, weather, and geographic conditions
- Ensure models meet safety and reliability standards for autonomous operation
- Publish internal technical documentation and present findings to cross-functional teams
- Stay current with advancements in computer vision and deep learning research
- Mentor junior engineers in best practices for machine learning development
- Participate in code reviews and maintain high software quality standards
- Use version control and experiment tracking systems effectively
- Deploy models into simulation and real-world testing environments
- Monitor model behavior in production and implement updates as needed
- Work within a safety-critical development framework compliant with industry standards
- Contribute to model interpretability and failure analysis efforts
Nice to Have
- Master's or PhD in a relevant technical discipline
- Experience with autonomous vehicle perception systems
- Background in stereo vision or structure-from-motion techniques
- Familiarity with LiDAR and sensor fusion concepts
- Experience with real-time operating systems
- Knowledge of automotive safety standards such as ISO 26262
- Contributions to open-source machine learning projects
- Publications in computer vision or machine learning conferences
Compensation
Competitive salary and benefits package commensurate with experience
Work Arrangement
Hybrid work environment with flexible scheduling options
What We Look For
- Candidates who combine strong technical skills with a practical mindset for real-world deployment
- Individuals who value precision, safety, and reproducibility in engineering decisions
Our Environment
- Fast-paced, engineering-driven culture focused on solving complex autonomy challenges
- Access to cutting-edge hardware and sensor suites for development and testing
Available for qualified candidates requiring sponsorship
