Your search has found 2 jobs
Staff ML Infrastructure Engineer (GPU & Distributed Systems)
Are you looking to scale GPU infrastructure up to and beyond 10,000 GPUs?
You'll help push an already high-performing team past their current operating level, using your skills and experience to scale training workloads, improve cluster reliability/usage and build systems that hold up under real pressure.
Your focus will be on distributed training and GPU infrastructure, making large-scale training actually usable for researchers—not just possible.
You'll be working across frontier model training, scientific workloads and robotics environments. So you're dealing with high-throughput systems and real-world constraints, not just controlled experiments.
You'll join a team that owns compute end-to-end—infra, systems, and operations—working closely with researchers to make training at this scale reliable.
They've raised over $500M, have real customers, and are now integrating models directly into robotics environments and beyond.
Key experience
  • Experience scaling GPU infrastructure from 2,000 to 10,000+ GPUs
  • Experience with Ray, Slurm or similar
  • Experience supporting core model training

The culture is collaborative and hands-on:
  • Strong focus on knowledge sharing and upskilling
  • Cross-team collaboration with researchers
  • 6-week cycles to allow deep focus and meaningful impact
  • A team that works hard but also likes to keep it fun
Up to $350k base + bonus + equity DOE
Remote across the US or hybrid options available in SF

All applicants will receive a response. 
Job type: Permanent
Emp type: Full-time
Salary type: Annual
Salary: negotiable
Job published: 08/04/2026
Job ID: 35635
Read More
Research Scientist - (Senior & Staff levels)
Want to push the boundaries of what reinforcement learning can achieve with frontier models?

In this role you will be advancing reinforcement learning methods for large-scale AI systems. You’ll be applying RL techniques to enhance reasoning, planning, and decision-making in models that directly impact fields from biology to climate and materials science.
Your work will combine RL with large language models, experimenting with RLHF, PPO, and DPO, designing evaluation frameworks, and fine-tuning models at scale. The aim is to go beyond benchmarks and deliver models that researchers can use to accelerate discovery.

You will be a driving force in a team that is building towards a broader superintelligence platform: models that don’t just generate text or data, but drive breakthroughs across multiple domains. As part of this, you’ll collaborate with domain experts to ensure your research translates into real-world scientific progress.

You should bring:
  • Deep expertise in reinforcement learning (policy optimisation, value-based, or model-based methods).
  • Experience applying RL to large models (RLHF, PPO, DPO).
  • Hands-on experience with model training and fine-tuning at scale.
  • PhD in Computer Science, Machine Learning, Robotics, or related field, with contributions to top-tier conferences (NeurIPS, ICML, ICLR, AAAI).
  • Experience with distributed computing platforms (cloud or HPC clusters).
  • Track record of running rigorous experiments and improving models based on results.

If you have experience with multi-agent RL, hierarchical/offline RL, or domain-specific work with scientific datasets you will be an ideal candidate for this position. 

Package: $250k - $400k base + bonus + stock 
Location: SF Bay area or potential for remote with travel to office when needed.

If you want to see your RL research power the next generation of superintelligence , this is the role for you!

 All applicants will receive a response.

 

Job type: Permanent
Emp type: Full-time
Salary type: Annual
Salary: negotiable
Job published: 20/10/2025
Job ID: 33780
Read More