Senior Staff ML Engineer, (TLM) Driver Understanding and Evaluation
Is this job for you?
Build my CV Build your CV and see how well you match this role — and every other one.
Track your applications on mobile The free Whileresume app, on iPhone and Android.
The role
Own the end-to-end strategy for ML-based evaluation metrics, ensuring scientific and statistical rigor across embodied AI applications.
Architect scalable systems to train and fine-tune large-scale generative models that simulate and evaluate driving behaviors.
Lead the design and iteration of novel RL algorithms, reward functions, and training paradigms for high-fidelity driving data.
Develop cutting-edge DL and Generative AI (LLM/VLM) solutions to streamline triaging, automate high-volume workflows, and analyze autonomous driving behaviors for anomalies.
Adopt and adapt Reinforcement Learning from Human Preference (RLHF) based data collection and evaluation practices.
Mentor engineers and align cross-functional teams—Driver Understanding, Simulation, System Engineering, Research, and Onboard Software—toward a cohesive evaluation strategy.
Architect scalable systems to train and fine-tune large-scale generative models that simulate and evaluate driving behaviors.
Lead the design and iteration of novel RL algorithms, reward functions, and training paradigms for high-fidelity driving data.
Develop cutting-edge DL and Generative AI (LLM/VLM) solutions to streamline triaging, automate high-volume workflows, and analyze autonomous driving behaviors for anomalies.
Adopt and adapt Reinforcement Learning from Human Preference (RLHF) based data collection and evaluation practices.
Mentor engineers and align cross-functional teams—Driver Understanding, Simulation, System Engineering, Research, and Onboard Software—toward a cohesive evaluation strategy.
See the full job post
Responsibilities, requirements, skills and benefits — create your free account.
or
Already have an account?
Log inYou might also like these jobs
No closely matching jobs yet — here are the most recent ones.