Research Scientist - Member of Technical Staff

AI Digest
$150,000 - $200,000Remote

About The Position

We're hiring a research scientist to lead on discovering interesting and important findings about AI capabilities and proclivities, for example using our >1 year of AI Village data and infrastructure to rapidly spin up new villages and environments to test hypotheses. We're hiring additional members of technical staff to help us scale up the Village, demonstrate the frontier of long-horizon agentic capabilities, and study the behaviour of models to surface insights about current and future capabilities, behaviours, and emergent dynamics.

Requirements

  • Generally very competent
  • A great LLM researcher – strong taste for sniffing out interesting and important questions, can quickly design and execute great analyses and experiments, and gets to the bottom of things
  • Has or could quickly develop good judgment on what’s important for how AI goes, especially with respect to the village
  • Has good epistemics – fits with an org culture of trying to figure out what’s true while moving fast
  • Independent and action-oriented – down to do lots of things even if outside your previous wheelhouse, with a bias for action. Once you settle in, you'll likely work fairly independently and be leading your own research efforts
  • Super interested in AI!

Nice To Haves

  • Cares deeply about AI going well
  • Good at managing researchers (if so, you could manage early career researchers via programs like MATS)
  • Thinks ASI within a decade is plausible and that the impacts will be transformative and potentially negative
  • Good writer (if so, you could flex into writing Village blogposts, or future AI Digest explainers)
  • Strong software engineer (if so, you can flex into working on the Village scaffolding and infrastructure)
  • Existing large audience, e.g. on social media (if so, you could help with that)

Responsibilities

  • Lead on discovering interesting and important findings about AI capabilities and proclivities.
  • Use AI Village data and infrastructure to rapidly spin up new villages and environments to test hypotheses.
  • Scale up the Village.
  • Demonstrate the frontier of long-horizon agentic capabilities.
  • Study the behaviour of models to surface insights about current and future capabilities, behaviours, and emergent dynamics.
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