Research Engineer

Job Description

This is not a black-box applied role: your work will be published, your infrastructure will be serious, and your impact will be direct. If you want to work at the frontier of generative modeling and physical AI, this is the team.

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 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

Are you interested in this position?

Apply by clicking on the “Apply Now” button below!

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