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P-1 AI

AI Product Engineer

Posted 13 Days Ago
Remote
Hiring Remotely in United States
200K-265K Annually
Senior level
Remote
Hiring Remotely in United States
200K-265K Annually
Senior level
Senior Software Engineer role focused on building AI capabilities for an engineering AGI product. Collaborate with a team to integrate cutting-edge machine learning into real-world engineering systems and shape product design.
The summary above was generated by AI

About you:

  • have done something remarkable, and have undeniable real-world proof-of-talent you can share with us

  • go from 0 → 1 on an idea before breakfast

  • always learning

  • believe in manifesting the future of physical engineering

About us:

We are building an engineering AGI. We founded P-1 AI with the conviction that the greatest impact of artificial intelligence will be on the built world. Our first product is Archie, an AI engineer capable of quantitative intuition over physical product domains and engineering tool use. Archie initially performs at the level of an entry-level design engineer but rapidly gets smarter and more capable. We aim to put an Archie on every engineering team at every industrial company on earth.

Our founding team includes the top minds in deep learning, model-based engineering, and industries that are our customers. We closed a $23 million seed round led by Radical Ventures that includes a number of other AI and industrial luminaries (from OpenAI, DeepMind, etc.).

This role is remote and you can be based anywhere in the US or Canada, where you must have existing work authorization. You will be expected to travel to our San Mateo office for co-working sessions approximately one week out of every six. If you are already located in the Bay Area or are interested in relocation, you are of course welcome to work out of our San Mateo office. Our AI team is based in the San Mateo office, so there would be some benefit to you being in-office at least part of the time.

In summary:

  • we are on a mission

  • multiple hats is the norm

  • no politics, low bureaucracy

  • fast, data-driven decision-making; velocity and agility are everything

  • believe in manifesting the future of physical engineering

About the role:

We are a small team tackling an ambitious problem. If we are successful, it will change the course of history. As such, we have a very high talent bar and are looking for people who have done something remarkable.

This is a research-meets-production role building the core AI capabilities of Archie. You will collaborate with AI researchers, software engineers, forward-deployed engineers, and domain experts to turn cutting-edge machine learning into real engineering systems used by the world’s leading manufacturers.

You will help train, adapt, and deploy large language models, build new agentic behaviors, and integrate Archie with industry-standard engineering tools. Your work will directly shape how physical products are designed and built.

We don’t care if you’ve done it before. We just need you to be brilliant, mission-driven, and thirsty to learn.

This role can be either remote (based in the US or Canada and with existing work authorization) or based in our SF office. If you are remote, you should plan to spend one week out of six co-working with the rest of the company in our SF office. We will support relocation for candidates interested in moving to SF.

Compensation:

$200k - $265k… for now. This role includes a significant equity component. We are an early-stage startup, so we favor equity over cash in our current compensation philosophy. You should too, or an early-stage startup might not be for you. That said, we expect cash compensation to progress quickly as the company matures.

Our benefits include healthcare, dental, and vision insurance, 401k with employer matching, and unlimited PTO.

Interview process:

  • Initial screening call (30 mins)

  • Biographical/behavioural interview (45 mins)

  • Technical interview (60 mins)

  • CEO interview (30 mins)

Top Skills

Engineering Tools
Large Language Models
Machine Learning

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