Research Engineer

Hedra; San Francisco (CA); On-site; USD 120,000 - 190,000; Full time

Benefits offered by this job

Competitive compensation and equity

401k (no match)

Healthcare (Silver PPO Medical, Vision

Lunch and snacks at the office

Job summary

Hedra is a pioneering generative modeling company in San Francisco, building a Physical AI team to translate models into real-world industry uses. As a Research Engineer on our Physical AI team, you will lead pre-training and post-training on action-conditioned world models, working closely with industrial partners to close the loop between AI and physical systems.

You will design training pipelines, refine methodologies, and build scalable distributed infrastructure using PyTorch, FSDP, and

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.

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.

Skills

Pre-training

Post-training

Distributed training

PyTorch

Education

BS/MS/PhD in CS/ML/Robotics

Tools

PyTorch

FSDP

DeepSpeed

Job description

Overview:

Hedra is a pioneering generative modeling company — first models to market — now building a Physical AI team to bring these models to real-world industry and economy use cases. As a Research Engineer on our Physical AI team, you will lead pre-training and post-training on action-conditioned world models, working hand‑in‑hand with industrial partners to close the loop between generative AI and physical systems. 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

Benefits:

• Competitive compensation and equity

• 401k (no match)

• Healthcare (Silver PPO Medical, Vision, Dental)

• Lunch and snacks at the office

We encourage you to apply even if you don't fully meet all the listed requirements; we value potential and diverse perspectives, and your unique skills could be a great asset to our team.