Research Engineer
Leads pre-training and post-training of action-conditioned world models and VLA models for physical AI applications. Requires PyTorch expertise, distributed training, and ML fundamentals; robotics background preferred.
About the job
Responsibilities
- Design, implement, and run pre-training and post-training pipelines for action-conditioned world models and vision-language-action (VLA) models
- Develop and refine training methodologies, including fine-tuning, reinforcement learning, and large-scale multimodal learning
- Design and generate training and evaluation datasets from simulation, including environment setup, domain randomization, and sim-to-real transfer strategies
- Build distributed training infrastructure using PyTorch, FSDP, and DeepSpeed
- Work with multimodal data pipelines involving video, sensory inputs, and action sequences
- Evaluate model performance using both benchmark datasets and real-world deployment metrics
- Contributions to research publications a plus
- Collaborate with industrial partners to adapt generative models for real-world physical AI applications
Qualifications
- Experience with pre-training or post-training on large generative models (video, multimodal, or action-conditioned)
- Hands-on proficiency with PyTorch and distributed training frameworks (FSDP, DeepSpeed)
- Strong fundamentals in machine learning, optimization, and large-scale data processing
- Familiarity with VLMs, VLAs, or world models
- Background in robotics, embodied AI, or sim-to-real transfer is a plus
- Experience with video understanding or temporal reasoning is a plus
- BS/MS/PhD in Computer Science, Machine Learning, Robotics, or a related field
Benefits
- Competitive compensation and equity
- 401k (no match)
- Healthcare (Silver PPO Medical, Vision, Dental)
- Lunch and snacks at the office
Skills
PyTorch, Fsdp, Deepspeed, Reinforcement Learning, Multimodal Learning, Vision-Language-Action Models, World Models, Sim-To-Real Transfer, Video Understanding, Vlms
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