Member of Technical Staff, Deployments

Introduction

We work with frontier labs, hyperscalers, and enterprises to help develop and deploy the next generation of AI agents. We see the creation of evals and environments, codifying human goals for agents, as the highest leverage human activity in the build up to ASI.

We've raised from Sequoia Capital, Bain Capital Ventures, Menlo Ventures, and SV Angel, are growing very quickly, and are off to the races to build the core infrastructure to unlock double digit economic growth with the next generation of agents.

A Brief Description of the Role

As Member of Technical Staff, Deployments you will collaborate closely with our entirely technical team to deliver robust environments, realistic data, and challenging tasks that are just of out of reach of today's frontier models.

Many of the projects will require herculean effort, combined with high attention to detail, and research taste. We want people who will do whatever it takes to solve immediate bottlenecks but also think in systems and are not content with local maxima.

You will not be a good fit if you:

• Thrive in routine, consistency, or structure, or you aren't good at context switching between tasks or priorities.

• Alternatively, we are looking for people who are excited to work on the frontier which is (almost by definition) filled with ambiguity and chaos. Clarity of thought and communication is a must-have.

• Seek to work in silos, manage, strategize, and plan without accountability for execution

• Alternatively, we are looking for people who aren't afraid to get their hands dirty and are excited to do a bit of everything across the entire tech stack, and across engineering and non-engineering work, including interacting with researchers and leaders at frontier labs

• Don't use AI, or aren't excited to spend a lot of time working with and improving AI agents

• Alternatively, we are looking for people great at managing many AI agents in parallel and understanding their capabilities, without outsourcing their own taste and judgment that will shape frontier model performance.

• Get things done for the sake of getting them done, or are fast and scrappy without care or focus on detail.

• Alternatively, we are looking for systems thinkers who don't sacrifice quality for speed; they deeply care about the work they put out, and therefore are incredibly fast but build systems that enable all of us to scale quality.

Experiencing founding, working early-stage, knowledge of AI evals are pluses.

We are on-site in San Francisco and New York.

A Brief Description of Us

Being an engineer at an early-stage startup is not easy. Here are some of our values and what we care about. If this excites you, we would love to chat.

• Own their outcomes: We succeed when our partners succeed. Therefore, we listen, build trust, and deliver value beyond what is contracted. We develop novel environments to study AI safety and capabilities. We post-train our own agents to share our learnings and best-practices. We study model behavior intensely and frequently report observations in our initial evaluation runs.

• Relentlessness: We are relentless in the speed at which we move when we build conviction, in getting in front of the right people, and in improving ourselves as a company and as individuals.

• Truth over comfort: Integrity to us means being honest with our partners, each other, and most importantly, ourselves. At the end of the day, early-stage startups are about the relentless pursuit of the truth, and we hold ourselves to a high standard to ensure we live that and succeed.

• Precision under pressure: We care deeply about delivering infrastructure to train safe, reliable agents. As such, a single detail in an environment can be the reason for unintentional failures within major training runs. Everyone on the team is fast and scrappy, but pays attention to every small detail. We don't cut corners; we cut scope and build systems

• Grit: A startup is built by people who like to get their hands dirty. We want to see that when left to your own devices, you know what to do and can stay resilient in the face of uncertainty and ambiguity.

Compensation

Salary range: $200,000–$600,000 USD annually, plus meaningful equity.