Proximal is building the research systems needed to identify what models can't yet do, build the tasks required to teach them, measure whether those capabilities improve, and continuously produce the data that frontier models need. We work with frontier AI labs to provide the data and evaluations behind their most capable models.
Our early team has built coding agents and RL infrastructure at companies like Cursor and Prime Intellect, worked at firms like Jane Street, and founded companies that raised millions.
We're growing extremely fast and are backed by top-tier funds including General Catalyst, alongside angel investors from OpenAI, Anthropic, xAI, Meta Superintelligence, Google DeepMind, and Thinking Machines.
About the role
As a software engineer, you'll build the core infrastructure and systems that power our data creation engine. We look for strong systems generalists who experiment fast with LLMs to build automations and engineer the robust, reliable infrastructure needed to let those automations run at scale.
What you’ll do
Build the infrastructure to run hundreds of thousands of concurrent agents reliably for 24+ hours
Build infrastructure to run ephemeral, production-like multi-node software systems as training environments, with a means of snapshotting and restoring progress
Build training infrastructure to support the development of specialized internal models
Build automated QA systems to adversarially evaluate tasks and agent outputs for correctness, fairness, and reward hacking
Build systems to continuously index all code on the internet
What we look for
Solid generalists who have designed and built systems from scratch where correctness, reliability, and performance mattered at scale
Comfortable working in research-heavy environments; you have strong experimental instincts and can work through ambiguous technical problems to deliver concrete engineering outcomes
Strong intuition for building automations and agentic systems; you can design and eng