AGI Inc
AI Researcher
11mo ago
USAmodel trainingsftrlhfdpoquantizationdistillation
Lead training of AI model families for on-device agents focusing on research in applied AI with emphasis on pretraining and post-training techniques.
About the company
- Build everyday AGI. Trustworthy, consumer-grade agents that redefine human–AI collaboration for millions. Software shouldn’t wait for commands; it should partner with you, amplifying what you can do every single day.
Conditions
- Competitive cash and meaningful equity. Top-tier relocation and immigration support. Permission to publish what's safe to publish. SF, in person.
How to apply
- Send a link to your most interesting result — paper, blog, model card, GitHub — with one paragraph on why it matters. Plus your resume, Google Scholar, or LinkedIn. Every exceptional candidate hears back within 48 hours.
Other
- We’re a stealth team of elite founders and AI researchers, with backgrounds spanning Stanford, OpenAI, and DeepMind . We’re industry leaders in mobile and computer-use agents, bringing these capabilities to consumer scale.
- Grounded in years of agent research, our AI is designed with trustworthiness and reliability as core pillars, not afterthoughts.
- We are supported by tier-1 investors who funded the first generation of AI giants; now they’re backing us to build the next: everyday AGI. (Watch the demo )
- If you see possibility where others see limits, read on.
- Frontier capability inside the compute and memory envelope of a consumer device — phone, laptop, wearable — is not a constraint. It's the most interesting research problem in applied AI today. You'll lead training for one of the model families that powers our on-device agents: pretraining recipe choices, post-training (SFT, RLHF, DPO, GRPO and whatever the next acronym ends up being), distillation, quantization, and the long tail of tricks that make a small model punch above its weight.
- This is for the researcher who's tired of training models that go behind an API. You want your model on the device in your pocket, your mom's pocket, and a hundred million pockets you'll never meet.
- One or more model capabilities end-to-end — from data mixture and training objective through eval and shipping into a production on-device runtime
- The experiment design and writeups that compound across the team — kill what doesn't move the metric, double down on what does
- A training workstream with a clear success metric and a checkpoint that ships
- Infra and product engineers, by turning research wins into shipped capabilities
- Partnerships, by telling them honestly what's possible at the next device refresh and what's not
- Other researchers, by reading their code and making theirs easier to read
- The training techniques that matter most for our regime — distillation from frontier teachers, MoE at small scale, speculative decoding, KV cache compression
- How to design experiments that move a number you actually care about
- What production model deployment looks like under hardware deadlines from OEM partners
- On-device tool use and agentic post-training at consumer scale
- The full stack from training run to phone
- After 30 days — You've reproduced one of our recent training runs end-to-end. You've named the three highest-leverage research bets for the next quarter and have a take on which two to run.
- After 60 days — You're leading a training workstream with a clear metric. You've shipped a checkpoint that beats the previous best on the eval that matters. People trust your read on what's working.
- After 90 days — Your work has shipped into a partner build. You've made one non-obvious bet that paid off and one that didn't, and the team has learned from both. You're shaping the next training cycle.