About the Role
The role involves conducting cutting-edge research to advance autonomous driving capabilities, focusing on machine learning, simulation, and system integration, with the goal of deploying safe and reliable self-driving technology.
Responsibilities
- Develop novel algorithms for perception, prediction, and planning in autonomous systems
- Design and implement machine learning models to improve system performance
- Conduct experiments to validate research hypotheses in simulation environments
- Collaborate with engineering teams to transition research into production
- Analyze real-world driving data to inform model development
- Improve simulation fidelity for training and testing autonomous vehicles
- Publish findings in top-tier research venues
- Evaluate emerging technologies relevant to self-driving systems
- Work on sensor fusion techniques combining camera, lidar, and radar inputs
- Optimize models for computational efficiency and real-time inference
- Contribute to the development of safety validation frameworks
- Engage in cross-functional discussions to align research with product goals
- Explore methods for handling edge cases in complex urban environments
- Advance understanding of behavior modeling for other road users
- Support the creation of synthetic data generation pipelines
- Investigate uncertainty quantification in perception and decision systems
- Help define metrics for measuring research impact
- Participate in code reviews and technical design discussions
- Stay current with advancements in AI, robotics, and autonomous driving
- Mentor junior researchers and interns when applicable
- Ensure ethical considerations are integrated into research practices
- Contribute to intellectual property development through patent disclosures
- Work with large-scale distributed computing infrastructure
- Apply reinforcement learning techniques to driving policy development
- Evaluate robustness of systems under diverse environmental conditions
Compensation
Competitive salary and equity package
Work Arrangement
Hybrid work model with flexibility
Team
Collaborative team of researchers and engineers in AI and robotics
Our Approach
- We combine deep learning, robotics, and physics-based simulation to build a new kind of autonomous trucking system
- Our methodology emphasizes safety, scalability, and efficiency through algorithmic innovation
Technology Stack
Python, PyTorch, TensorFlow, ROS, C++, CUDA, Docker, Kubernetes, AWS
Research Culture
- We value rigorous science, peer review, and open collaboration across teams
- Researchers are encouraged to publish and present at leading conferences
Impact Focus
Every research initiative is evaluated for its potential to improve safety and operational performance
Growth Opportunities
- Opportunities to lead research directions and mentor team members
- Access to compute resources and real-world testing environments
Visa sponsorship available for qualified candidates
