SID trains AI that can retrieve and reason over any data source. Intelligence and skills are inconsequential without context. Today, AI is blind to information that is not on the internet. If we want AI to solve real problems, we need to change that.
SID.ai
is backed by Y Combinator, Canaan, Rebel, and General Catalyst – as well as a great set of angels.
If you don't match all of the requirements, we still encourage you to apply. We care much more about potential and the rate of improvement than achievements. We train and invest in our people!
Responsibilities
Post-train reasoning into LLMs with GRPO and SFT.
Design and iterate RL training environments for retrieval – unstructured, structured, web.
Run small and large model experiments – yolo runs encouraged.
Work on next-generation vision-first embedding models.
Perks
Work on frontier methods that scale. No weird old-school AI.
Everyone on the team can code – this might change in the future of course.
Requirements
Not afraid of formulas – a technical major is an indicator of this (but isn't the only one).
Thinks they can learn anything in 2 weeks, but isn't arrogant about it.
Prefers .py to .tex
Familiar with RL pipelines for language models
Comfortable with torchrun/accelerate/multi-node training.
Clever about getting the data needed – or synthetically generating it.
Finds easy solutions to hard problems, but doesn't mind getting their hands dirty, i.e., jumping a layer down into PyTorch or CUDA.
Familiar with 'You and Your Research.' Understands what it takes to do significant work.
Must articulate ideas well! A big part of making successful models is telling people about them. This includes writing docs and technical reports at the minimum – and jumping on podcasts at the extreme.
Things you should know
We look for people who never settle. People who always believe better is possible.
Startup work is always intense and sometimes frustrating: The nature of working on novel ideas is that not all of them pan out. It can be that you put blood, sweat, and tears into a feature or model and it just ends up not working through no fault of your own.
We might publish, but cannot guarantee that we will.
The role is in-person only from our office in SF.