Research Engineer

San Franciscofull-timemid

OtherResearch Engineer

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Quick Summary

Overview

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.

Key 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…

Requirements Summary

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,…

Technical Tools

pytorchetlmachine-learning

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

~1 min read

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Design, implement, and run pre-training and post-training pipelines for action-conditioned world models and vision-language-action (VLA) models
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Develop and refine training methodologies, including fine-tuning, reinforcement learning, and large-scale multimodal learning
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Design and generate training and evaluation datasets from simulation, including environment setup, domain randomization, and sim-to-real transfer strategies
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Build distributed training infrastructure using PyTorch, FSDP, and DeepSpeed
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Work with multimodal data pipelines involving video, sensory inputs, and action sequences
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Evaluate model performance using both benchmark datasets and real-world deployment metrics
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Contributions research publications a plus
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Collaborate with industrial partners to adapt generative models for real-world physical AI applications

Requirements

~1 min read

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