Harnham
AI Research Engineer, LLMs and Large Scale Training
Skills
6 days ago
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Important information
Contract type:
Permanent
Location:
United States
Work mode:
Remote
Published on:
22 October 2026
What they need
Every major AI lab is chasing the same milestone: AI that can do real research. This team is building it now, at startup speed.
Our client is an early stage AI lab backed by a top tier VC and founded by people from leading AI labs and universities. They’re developing AI systems capable of doing research work. Behind every result is infrastructure, and that’s where you come in.
The team is small and senior. Engineers and researchers sit on the same team, and what you build gets used straight away.
Location: Hybrid in San Francisco or New York preferred, with fully remote considered for the right person.
Where you’d focus
- Scaling workloads across large GPU clusters
- Building training infrastructure for large models
- Cutting the time and cost of training and running models
- Monitoring and debugging for large training runs
- Evaluation tooling for large language models
Requirements
- 5+ years building ML systems used in production
- Distributed training or large scale ML infrastructure experience
- A record of shipping infrastructure other teams depend on
- Python, plus PyTorch or JAX
Preferred
- GPU performance, inference or CUDA work
- A Master’s or PhD in machine learning, computer science or a related field
- Startup or early stage company experience
- Real world experience beyond university or large company settings
Why it’s worth a conversation
- An early seat on one of the most ambitious goals in AI
- Your work shows up in every result the team ships
- Compute to match the ambition, plus meaningful equity
- Work next to top researchers
Best suited to people who
- Want more scope than a large company can offer
- Have shipped in a fast moving, small team environment
- Like owning a system from first build to production
About this search: We’re running this search exclusively and on a retained basis, working as an embedded part of the client’s hiring team. All candidates for these roles come through us, and the client’s name is shared after an initial conversation.
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