SID
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About
SID is an AI company focused on providing crucial context for artificial intelligence. They believe that while AI offers learning, reasoning, and acting capabilities, intelligence is limited without proper context. At SID, they train models to deliver this context, aiming to connect intelligence to the real world. Their flagship product, SID-1, is an agentic retrieval model designed to efficiently find and provide the right context. SID-1 boasts 1.8x better recall and is 24x faster than previous methods. It also doubles embedding-only accuracy and outperforms other frontier models on highly complex retrieval tasks. The company is a lean team based in San Francisco, backed by notable investors like Y Combinator and Canaan.
Platform
Features
• connects intelligence to context
• outperforms frontier models on complex retrieval tasks
• doubles embedding-only accuracy
• 24x faster retrieval
• 1.8x better recall
• agentic retrieval model (sid-1)
Job Opportunities
Research Engineer
SID is an AI company training models to provide context for intelligence, featuring SID-1, its first agentic retrieval model for enhanced data recall and speed.
Benefits:
Non-bureaucratic compute approvals
Work on frontier methods that scale
Everyone on the team can code
Competitive compensation with generous early-stage equity, full medical and vision
Education Requirements:
BSc/MSc/PhD is an indicator
Experience Requirements:
Familiar with TRL/veRL/etc.
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.
Publications are a plus, but being able to critically evaluate research is a must.
Other Requirements:
Not afraid of formulas
Thinks they can learn anything in 2 weeks, but isn't arrogant about it.
Prefers .py to .tex
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.
If you do not have US work authorization, we can help with that.
Responsibilities:
Post-train reasoning into LLMs with GRPO and SFT.
Design and iterate RL training environments for retrieval – unstructured, structured, web.
Own the entire training pipeline: from training data curation to wandb.
Run small and large model experiments – yolo runs encouraged.
Work on next-generation vision-first embedding models.
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Ratings & Reviews
Average Rating: 5.0
Average Rating: 5.0
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User Reviews
9/9/2024
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