🛠 Tech Stack

💼 About This Role

You'll bridge the sim-to-real gap for autonomous truck systems by translating simulated data into realistic BEV embeddings. You'll optimize training pipelines and enable closed-loop reinforcement learning for planning models. This role offers hands-on impact at a Fast Company Most Innovative Company.

🎯 What You'll Do

• Develop translation layer converting simulator GT to noisy BEV embeddings
• Implement shortcut pipeline bypassing slow rendering for BEV features
• Integrate BEV embeddings into training pipeline for planning models
• Create infrastructure for self-play RL fine-tuning in BEV feature space

📋 Requirements

• Deep learning foundation in computer vision and machine learning
• Proficiency in Python and PyTorch
• Experience with BEV or end-to-end autonomous driving architectures
• Experience with sim-to-real gap in autonomous systems

✨ Nice to Have

• Experience with C++
• Experience with end-to-end driving models
• Experience with simulators like CARLA or IsaacSim

📨 Hiring Process

Estimated timeline: 2-4 weeks · AI estimate

1. 1Recruiter Screen· 30 min
2. 2Technical Interview· 60 min
3. 3Hiring Manager Interview· 45 min