Research Engineer

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
• Create multimodal data pipelines involving video, sensory inputs, and action sequences
• Evaluate model performance using both benchmark datasets and real-world deployment metrics
• Contribute to research publications
• 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
• Experience with video understanding or temporal reasoning
• Bachelor's, Master's, or 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