Posting record
Open 17+ days
- Hedra's site dates the posting
- Ghostless first sees this listing
- Last checked: still up
How we know
It hasn't been reposted, listed twice, or left up unusually long, and the posting doesn't say it's a talent pool.
Ghostless records the day it first sees a posting, so a repost can't reset its age. This one was already up when Ghostless started tracking Hedra, so its true age is longer.
Last checked Nov 2, 2026.
How well do you match?
See how much of this posting your resume shows, and what it's missing.
Upload your resumeWhat it asks of you
- PhD asked for
What the posting discloses
- Pay
- $175k to $275k a year USD
Not stated: Real vacancy, AI screening.
Why these three
Ontario requires postings from employers with 25 or more staff to state the pay, whether the vacancy exists, and whether AI screens applicants.
Skills mentioned
About the role
About Hedra
Hedra is a research and infrastructure lab advancing post-training and inference for multimodal and physical AI. Our ambition is to bring recursive self-improvement to models that learn from and interact with the real world.
We combine reinforcement learning research with GPU compute and optimized inference to help teams post-train, serve and continuously improve models they own.
We're backed by Index Ventures, a16z and Abstract Ventures. Our team works fully in person in San Francisco.
We're looking for people with startup experience who take ownership of their work and care deeply about the problems our customers need to solve. You'll help build the technology, products and business that turn this research into a lasting company.
What You'll Do
- 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.
Something wrong with this posting?