- Workplace
- Remote
- Type
- Full-time
- Department
- Engineering
- Education
- Master
- Source
- RecruiterFlow
Description
Pantheon is building general-purpose robots. Research velocity — experiments shipped per week — is the primary bottleneck on how fast Pantheon gets there. Research Engineers directly determine that rate.
The Opportunity
Pantheon is looking for a Research Engineer to work alongside researchers on large pretraining runs, RL post-training, and the data and infrastructure that make them scale. You will own the loop from the H100 training cluster to robots running on a factory floor.
This is a role for a strong software engineer first, with deep ML intuition — someone who has implemented models end-to-end, not just run existing ones. If you want your engineering work to directly determine how fast a robotics frontier gets pushed, this is that role.
What You'll Do
- • Implement model architectures and training recipes alongside researchers and make them run efficiently at scale
- • Build and maintain data pipelines including collection, curation, filtering, and augmentation across vision, proprioception, action, and language
- • Own training infrastructure including distributed training, checkpointing, profiling, and debugging across the GPU fleet
- • Build evaluation that catches regressions and produces real signal, both offline and on-robot
- • Own the loop from training cluster to deployed robot and close it with data from the field
You Should Have
- • Strong software engineering foundations with deep ML intuition
- • Experience implementing ML models end-to-end, not just running existing ones
- • Fluency in PyTorch or JAX with hands-on distributed training experience
- • Ability to debug across the full stack
- • Comfort moving fast in ambiguity
Nice to Have
- • Experience with large-scale training infrastructure including multi-node, multi-GPU, and cluster environments
- • Prior work on large multimodal models
- • Publications at NeurIPS, ICML, ICLR, CoRL, RSS, ICRA, or similar venues
- • Experience deploying models on physical hardware and optimizing for latency and compute at the edge
