PhD Research Scientist
5 days ago
The Post-Training Team is dedicated to optimising AI systems to achieve state-of-the-art performance across the various risk domains that AISI focuses on. This is accomplished through a combination of scaffolding, prompting, supervised and RL fine-tuning of the AI models which AISI has access to.
To improve confidence in our assessments, we make significant effort to enhance the model's performance in the domains of interest.
For many of our evaluations, this means taking a model we have been given access to and embedding it as part of a wider AI system—for example, in our cybersecurity evaluations, we provide models with access to tools for interacting with the underlying operating system and repeatedly call models to act in such environment. In our evaluations which do not require agentic capabilities, we may use elicitation techniques like fine-tuning and prompt engineering to ensure assessing the model at its full capacity.
As a member of this team, you will use cutting-edge machine learning techniques to improve model performance in our domains of interest. Our Agents sub-team focuses on developing the LLM tools and scaffolding to create highly capable LLM-based agents, while our Finetuning Team builds out finetuning pipelines to improve models on our domains of interest.
The Post-Training Team is seeking strong Research Scientists to join the team. The priorities of the team include both research-oriented tasks—such as designing new techniques for scaling inference-time computation or developing methodologies for in-depth analysis of agent behaviour—and engineering-oriented tasks—like implementing new tools for our LLM agents or creating pipelines for supporting and fine-tuning large open-source models. We recognise that some technical staff may prefer to span or alternate between engineering and research responsibilities, and this versatility is something we actively look for in our hires.
In addition to junior roles, we offer Senior, Staff, and Principal Research Engineer positions for candidates with the requisite seniority and experience.
Experience conducting empirical machine learning research (e.g. PhD in a technical field and/or papers at top ML conferences), particularly on LLMs.
Experience with machine learning engineering, or extensive experience as a software engineer with a strong demonstration of relevant skills/knowledge in the machine learning.
Building LLM agents in industry or open-source collectives, particularly in areas adjacent to the main interests of one of our workstreams e.g. in-IDE coding assistants, research assistants etc. (Leading research on improving and measuring the capabilities of LLM agents (for our Agents sub-team)
Building pipelines for fine-tuning (or pretraining LLMs). Finetuning or pretraining LLMs in a research context, particularly to achieve increased performance in specific domains (for our finetuning subteam).
We are hiring individuals at all ranges of seniority and experience within the research unit, and this advert allows you to apply for any of the roles within this range. There are a range of pension options available which can be found through the Civil Service website.
Research problem selection
Research Engineering
Writing code efficiently
Python
Frontier model training knowledge
AI safety research knowledge
Learn through coaching
Chemistry or Biology
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