5-8 years of expertise in Deep Learning, particularly in VLM, VLA, or Embodied AI.
Proven experience deploying Foundation Models (Transformers, LLMs) at scale.
Strong understanding of Sequential Decision Making, World Models, or Policy Gradient methods.
Mastery of PyTorch and experience with distributed training techniques like DeepSpeed or Megatron.
Possess a 'Product-First' mindset balancing research with the practical needs of L4 production vehicles.
Responsibilities
Lead the design of sophisticated VLA architectures for multi-modal perception and action generation.
Drive research in generative world models to enhance closed-loop training simulations.
Refine driving policies using Advanced RL and IL methods, focusing on complex planning.
Define scaling laws for driving foundation models, overseeing data curation and automated labeling.
Lead model adaptation strategies for diverse global road conditions and traffic laws.
Benefits
Supportive and engaging work environment.
Access to robust infrastructures for ML model development and research.
Collaborate with top talent on cutting-edge technology.
Contribute to transforming the transportation landscape through advancements in autonomous driving.
Free snacks, lunches, dinners, and fun activities.
Full Job Description
The Mission: We are building the next generation of L4 autonomous vehicles. Moving beyond traditional modular stacks, we are developing large-scale Vision-Language-Action (VLA) models and World Models to handle the infinite long-tail scenarios of global driving. As a Senior Staff Machine Learning Engineer, you will architect the transition from behavior cloning to intelligent, zero-shot decision-making in diverse global markets.
Key Responsibilities:
Architectural Leadership: Lead the design of end-to-end VLA architectures, bridging multi-modal perception with high-level linguistic reasoning and precise action generation.
World Model Development: Drive R&D in generative world models (latent dynamics) to create high-fidelity, controllable driving simulations for closed-loop training and evaluation.
Policy Evolution: Apply Advanced RL (Online/Offline) and IL to refine driving policies, focusing on long-horizon planning and complex multi-agent interactions.
Scaling & Data Strategy: Define scaling laws for driving foundation models, overseeing data curation, automated labeling, and post-training at a multi-billion parameter scale.
Global Generalization: Lead the model's adaptation strategy for overseas road conditions, ensuring robust performance across varying traffic laws and driving cultures.
Qualifications:
5-8 years of expertise in Deep Learning, with a significant track record in VLM, VLA, or Embodied AI.
Proven experience in training and deploying Foundation Models (Transformers, LLMs) at scale.
Deep understanding of Sequential Decision Making, World Models, or Policy Gradient methods.
Mastery of PyTorch and expertise in distributed training (DeepSpeed, Megatron, etc.).
A "Product-First" mindset: The ability to balance cutting-edge research with the deterministic requirements of L4 production vehicles.
What do we provide:
A fun, supportive and engaging environment
Infrastructures and computational resources to support your ML model development/research.
Opportunity to work on cutting edge technologies with the top talent in the field.
Opportunity to make significant impact on transportation revolution by the means of advancing autonomous driving
Competitive compensation package
Snacks, lunches, dinners, and fun activities
The base salary range for this full-time position is $244,140-$413,160, in addition to bonus, equity and benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training.
