SID Verified
IT Services, Software Development, Digital Transformation, Technology Consulting
Research Intern (Summer 2026)
San Francisco, California, United StatesOnsiteInternshipJunior / Entry-level$5,000–$10,000 /yrPosted 1 month agoVisa sponsorship availableHidden Gem · YC Startup
Role summary
SID is a Y Combinator-backed startup developing AI that can retrieve and reason over any data source, aiming to overcome the internet-bound limitations of current AI. They are seeking Research Interns for Summer 2026 to work on cutting-edge AI methods, including post-training LLMs with GRPO and SFT, designing RL training environments, running model experiments, and developing vision-first embedding models. The role emphasizes learning agility, problem-solving, and strong communication skills, with a focus on practical application in PyTorch and CUDA. This is an in-person role at their San Francisco office.
**_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](http://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.
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.
