Job Description

Current LLMs are impressive, but they still struggle to reason over long time horizons, maintain context, and operate reliably across complex tasks.

This team is tackling those problems head on.

They're building a new generation of foundation models designed for persistent state, long-context reasoning, and agentic workflows. Rather than optimising today's architectures, they're exploring what comes next, creating models that can plan, reason, and stay coherent across interactions that stretch far beyond today's context windows.

You'll join the research team focused on post-training, reinforcement learning, and model behaviour. Your work will directly influence how these models learn after pre-training, improving reasoning, planning, evaluation, and long-horizon performance while running experiments at a genuinely significant scale.

Your work will include:

• Researching RL techniques for LLM post-training and reasoning
• Designing large-scale experiments and evaluation frameworks
• Improving model behaviour across long-context and agent-style tasks
• Training and evaluating foundation models ranging from 20B to 300B+ parameters

We're looking for researchers who have already worked on challenging problems such as RL for language models, post-training, model evaluation, or large-scale experimentation. If you've helped push models beyond benchmark performance and care about how they behave in real-world settings, you'll be in good company.

You'll have access to substantial compute, a highly technical research team, and the freedom to explore ambitious ideas that can shape the direction of future foundation models.

Salary: Up to circa $400,000 base plus meaningful stock (package negotiable depending on experience)

Location: Remote across the US or hybrid in San Francisco.

If you'd like to find out more, we'd be happy to have a confidential conversation.

All applicants will receive a response.