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Member of Technical Staff (Post Training)

inherent· Technical Staff
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LondonFull-timePosted 5mo ago

About the role

Member of Technical Staff to lead work on post-training state-of-the-art foundation models for open-ended agentic capabilities in scientific research. Involves data sourcing, autocurricula, SFT/RL algorithms, tooling for self-improvement, results analysis, and hypothesis-driven iteration. On-site in London.

Responsibilities

  • Design, implement, and tune SFT and RL algorithms to post-train models that autonomously perform state-of-the-art research.
  • Build the autocurricula, judges, harnesses and eval pipelines that turn open-ended research tasks into reliable reward signal.
  • Run large-scale experiments on state-of-the-art hardware and analyse experiments to determine the next hypotheses to test, in collaboration with our AI agents.
  • Close recursive loops so that AI agents drive their own post-training research.
  • Work closely with colleagues in the Infrastructure and AI for Science teams to optimise hardware and deliver remarkable performance in real scientific domains.

Qualifications

  • 3+ years of deep learning research experience.
  • Experience post-training large language, vision, video or multi-modal models.
  • Demonstrated track record of success in deep learning research, whether papers, model releases, open-source contributions, or other artifacts.
  • 5+ years of software engineering experience, including deep familiarity with Python and at least one deep learning framework (e.g., PyTorch, JAX).
  • Experience using the latest coding agents, and opinions about optimal workflow.
  • Enthusiasm for experimental organizational design.
  • AI-pilled: adopting agents, keen to build a company where agents are front and centre.

Skills

Required:Deep learning researchPost-training of large models (LLMs/vision/multimodal)Software engineering
Last updated Aug 20, 2026. Interested? Apply on the company site.
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About inherent

Recursively self-improving to discover new knowledge.

Recursively self-improving to discover new knowledge.

AIlabresearchinstitutioncollective intelligencerecursive self-improvementAI-driven scientific discovery
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