Member of Technical Staff, Platform

Ambitus works with enterprises and frontier AI labs to improve how AI performs in professional settings, starting with engineering and life sciences. The mission is to automate white-collar work at firms where the gains show up in daily life, in physical engineering and life sciences, more than in purely digital tools. Customers are mid-market enterprises doing more than $50 million in revenue, and frontier labs. Ambitus is already working with three of the leading labs. People from Ambitus sit inside enterprises and automate workflows. On the back end, an automated eval platform builds evals for those custom automations, which shows where models fail and feeds the reinforcement-learning environments Ambitus sells to the labs. The loop is to embed with a company, find the failures, sell the data that improves the models, then expand inside that enterprise.

Ambitus raised a $4 million seed from Neo, with angels from OpenAI, Thinking Machines, and Summit Partners. It is at an eight-figure run rate and growing fast. The team is 6 people, going to 8, with an end-of-year goal around 15. A co-founder was early at Mercor, and the founding group includes Stanford and BCG backgrounds. Seed stage, about 1 to 10 people, founded in 2026, in San Francisco.

Ambitus is hiring a member of technical staff for the platform. You build the systems that make the motion possible: how agents run, how experts review their work, and how results feed future datasets and deployments. The company lives or dies on how reusable the infrastructure is across contracts, so every contract should get easier, especially on evals. The work is distributed systems, data pipelines, and performance, plus the tools experts use to write and review tasks. Agent runs can take hours and depend on model APIs, remote environments, and background services. The job is to make those workflows reliable, help the team understand failures, and keep performance up as usage grows. The goal they name is trillions of tokens of processing per week. Sandbox reliability is a live pain point.

This seat is where they want to be thoughtful rather than moving as fast as the other roles. They lean toward someone from a team with exceptional engineering standards, because startups often skip good practice on infrastructure. A strong alternative is someone who was very early at a startup that later grew, and who had to stand up and scale the infrastructure themselves.

You own long-horizon agent workflows. You own the services and data pipelines for task execution, evaluation results, and dataset delivery. You investigate production issues and improve performance, resource usage, and recovery. You own reliability for agent runs that span hours. You build the internal tools experts use to write and review tasks, so less of that work is repetitive and manual.

Bar: about 3 to 7 years. Production software with a strong grounding in distributed systems, data pipelines, or performance. You investigate unfamiliar problems across a codebase. You have taken a project from requirement through implementation and release. Agent workflows, model inference, or evaluation systems are bonuses. An exceptional-team pedigree with a real quality bar, having stood up and scaled infrastructure early at a startup that grew, and making long-running failure-prone systems reliable are green flags. Hand-waving infrastructure quality, no real distributed-systems depth, or needing fully defined problems is a miss.

In office in San Francisco. Visa sponsorship is limited. Equity is about 0.1% to 0.5%. A free gym membership, health, dental, and vision, and a $1,000 a month meals stipend. Posted work intensity is 9-9-6. The process is a one-hour whiteboard system design, a one-hour open-ended coding problem where AI agents are allowed, a one-day on-site, then an offer.