Software Engineer

Proximal; San Francisco, Northern (CA, KY); Hybrid; USD 140,000 - 210,000; Full time

Job summary

Proximal is building the research systems needed to identify what models can't yet do, build the tasks required to teach them, measure improvements, and continuously produce the data frontier models need. We are assembling an early team with deep experience building coding agents, RL infrastructure, and scalable systems.

As a software engineer, you'll develop the core infrastructure powering our data creation engine, focusing on reliability and automation in a research-driven setting.

Qualifications

• Experience designing and building large-scale systems with reliability and performance.
• Ability to work in research-heavy environments and handle ambiguous technical problems.
• Skill in building automations and agent-based systems.
• Strong system design judgment for tradeoffs in performance, reliability, and cost.
• Self-directed with ownership and rapid execution without heavy oversight.

Responsibilities

• Build infrastructure to run hundreds of thousands of concurrent agents reliably for 24+ hours.
• Create ephemeral, production-like multi-node training environments with snapshotting and restore capabilities.
• Develop automated QA to adversarially evaluate tasks and agent outputs for correctness, fairness, and reward hacking.
• Build systems to continuously index all code on the internet.

Skills

Systems design

Reliability at scale

Research-friendly mindset

Automation / agent systems

Experimentation

Ownership

Job description

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 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

• Strong 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 engineer reliable systems that solve complex tasks
• Excellent systems intuition and design judgment; you can reason through tradeoffs in performance, reliability, complexity, and cost
• Self-directed and relentless, you take ownership of problems end-to-end without heavy process or oversight
• Execution with excellence, even at high speed. We don't think that shipping fast has to come at the cost of quality and reliability.