Research Engineer

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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Mental health support

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Relocation assistance

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Childcare support

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Learning budget

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Salary

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401(k) match

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Market context

U.S. role benchmark (BLS OEWS)

$116,543 U.S. median for this role

Projected growth (BLS Employment Projections)

+9.8% - Much faster than average

Matched to SOC 15-1252 - Software Engineering aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Engineering From the posting source checked Jun 20, 2026

Seniority

Mid From the posting source checked Jun 20, 2026

Schedule

Shift type

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Weekend work

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Company

Company stage

Early From the posting source checked Jun 20, 2026

Equity

Offered From the posting source checked Jun 20, 2026

Application

Cover letter

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Assessment

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Deadline

Not stated

Where they hire

State eligibility is not yet verified.

About this role

Research Engineer San Francisco, California, United States 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

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