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HedraHedraSan Francisco, CA

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.

175k – 275k
On-siteML Engineering

About the role

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

PyTorchFsdpDeepspeedReinforcement LearningMultimodal LearningVision-Language-Action ModelsWorld ModelsSim-To-Real TransferVideo UnderstandingVlms

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