Lead End-to-End Vision-Language Driving AI

Neolix; Sunnyvale (CA); On-site; USD 250,000 - 420,000; Full time

Benefits offered by this job

Founding-team equity

Premium health, dental, vision

401(k)

Job summary

Neolix US is seeking a hands-on technical leader to own the next generation of end-to-end driving using a Vision-Language-Action model family. You will map camera inputs to driving actions, underpin training pipelines and drive validation in real vehicles.

You will lead cross-functional teams, apply state-of-the-art multimodal modeling and ensure scalable, safe integration with vehicle systems across a global program.

Qualifications

• Hands-on experience designing, training and evaluating end-to-end driving, robotics-policy or VLA models mapping raw sensor input to actions.
• Experience at leading autonomous-driving company required.
• Strong knowledge of model training and evaluation with real-vehicle deployments.

Responsibilities

• Lead the design of the end-to-end VLA driving model, including observation and action interfaces, backbone selection, temporal modeling, action representation and training objectives.
• Own the full training chain (pre-training, post-training and closed-loop training) using imitation learning, learning from interventions and reinforcement learning.
• Establish rigorous closed-loop evaluation, with reproducible baselines, ablations, held-out distributions and a behavior failure taxonomy.
• Work with data, training infrastructure, vehicle and safety teams to turn research results into validated vehicle integration.
• Set an ambitious, testable technical roadmap and raise the scientific bar of our global team.

Skills

Autonomous driving

PyTorch/JAX

Multimodal modeling

Temporal modeling

Leadership

Vehicle integration

Education

Technical degree

Tools

PyTorch

JAX

Job description

Neolix US is seeking a hands-on technical leader to own the next generation of end-to-end driving using a Vision-Language-Action model family. You will map camera inputs to driving actions, underpin training pipelines and drive validation in real vehicles.

You will lead cross-functional teams, apply state-of-the-art multimodal modeling and ensure scalable, safe integration with vehicle systems across a global program.