Software Engineer

ApplyMint; San Francisco (CA); On-site; USD 150,000 - 220,000; 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 whether those capabilities improve, and continuously produce the data that frontier models need.

As a software engineer, you'll build the core infrastructure and systems that power our data creation engine, working with LLMs to create automations and reliable infrastructure that can run at scale.

Qualifications

• Strong systems generalist with experience building scalable infrastructure.
• Experience working in research-heavy environments.
• Ability to reason about performance, reliability, and cost.

Responsibilities

• Build infrastructure to run hundreds of thousands of concurrent agents for 24+ hours.
• Build infrastructure to run episodic, 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.

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 episodic, 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

• 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.