AI Engineer
AGI, Inc.
AI Engineer
AGI, Inc.
Advanced AI for natural language understanding
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.
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.
Build the systems our agents run on. Make them feel like magic, not infrastructure.
Our agents are only as trustworthy as the backend behind them. Every API call, every retry, every queued job is the difference between an agent that feels alive and one that feels like a chatbot. You'll own the systems that turn research into product — model orchestration, agent state, partner integrations, the data plane — and you'll make them fast enough, observable enough, and boring enough that the rest of the team can ship without thinking about them.
This is for the backend engineer who has been the on-call hero before, who has strong opinions about queues and idempotency, and who wants to design infrastructure for a product where the floor is "millions of devices."
The agent backend end-to-end — model orchestration, tool-use plumbing, agent state, retries, observability
The data plane behind every agent action — Postgres schema, caching, queues, event streams — at OEM scale
Production SLAs, on-call, and the reliability bar for everything the agent touches in the cloud
Research, by shipping their work to real users in days, not quarters — and feeding back what breaks in production
Forward-deployed engineers, by giving them backends partners can integrate against without a six-week meeting
iOS and Android, by drawing the right line between on-device and cloud so neither side carries the wrong weight
How to design backends for LLM-powered systems where latency, cost, and non-determinism are first-class concerns
How to run a production system you'd happily put your name on — observability, incident response, capacity planning
The internals of agentic systems from the people who published the canonical papers on them
What it takes to run agent infrastructure at OEM scale, across Samsung, OPPO, Lenovo, and Vertu devices
On-device / cloud co-design — when to push compute to the phone and when to keep it on our side
After 30 days — You've shipped a meaningful change to the agent backend that a user would feel — latency, reliability, or a new capability. You can name the three weakest links in our infra and have started fixing one. You've taken a real on-call shift.
After 60 days — You own a major surface of the backend. A research breakthrough has shipped through systems you designed. Other engineers route their hardest backend questions to you. You've set the reliability and observability bar that new services have to meet.
After 90 days — Our agent backend is something you'd happily defend to an infra engineer at a top-tier consumer company. You've shaped the architecture for one of our partner launches and have a strong, opinionated plan for what our infra needs to look like to support 10M devices.
Competitive cash and meaningful equity. Top-tier relocation and immigration support. SF, in person.
Send a link to a backend system you've built or operated, with one paragraph on a production decision you'd defend on a whiteboard. Plus your resume, GitHub, or LinkedIn. Every exceptional candidate hears back within 48 hours.
AGI, Inc. develops and deploys highly capable artificial intelligence systems focused on natural language understanding. Its product is a software system that processes language using deep learning models to understand and answer questions, aiming to perform at human-level accuracy on standard NLU benchmarks such as SQuAD. Unlike many AI developers that target broad capabilities, AGI, Inc. centers on advancing NLU performance on established datasets, with the goal of solving core language understanding challenges. The company measures its progress by achieving human-level results on benchmark datasets and applying its technology to real-world language tasks.
Explore more opportunities at AGI, Inc.