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P-1 AI

AI Research Scientist - Spatial Reasoning

Posted 24 Days Ago
Be an Early Applicant
Remote or Hybrid
Hiring Remotely in CA
Senior level
Remote or Hybrid
Hiring Remotely in CA
Senior level
Lead applied research on spatial reasoning, geometric and physical world modeling. Own data generation, evaluation, and product integration. Implement experiments, build production code, and drive research strategy in 3D, multimodal, simulation, and related domains.
The summary above was generated by AI

TL;DR: If you:

  • think deeply about how machines reason about the physical world;

  • have a track record of exceptional research or engineering achievement;

  • move effortlessly between papers, experiments, and production code;

  • love difficult problems with no obvious solution…


… you should apply for this role!

About P-1 AI:

At P-1 AI, we are building an AI engineer agent for the physical world named Archie. We maximize Archie’s anthropomorphism so that he fits seamlessly into existing engineering teams and workflows in the form factor of a human engineer. Archie today is at the level of a junior mechanical and electrical engineer, with a quantitative intuition over the product design space and the ability to use complex engineering tools—the same tools his human teammates use. Archie's tech stack includes a custom agentic harness, structured design representation, continual skills learning, and small custom post-trained models (SFT and RLVR) using proprietary semi-synthetic training data sets and environments which create a deep competitive moat. Our ultimate aim is to build engineering ASI. We are backed in our mission by some of the top venture investors and AI luminaries.

About the opportunity:

We’re seeking an exceptional AI Research Scientist to join our small team of elite researchers and help us push the boundaries of AI applied to the physical world. This role blends cutting-edge AI research with hands-on engineering, and is ideal for someone who thrives at the intersection of ideas and implementation.

About the role:

  • Research and develop new approaches for spatial reasoning, geometric understanding, physical reasoning, and structured world modeling in large-scale AI systems.

  • Own the full development pipeline from data generation to evaluation to product integration.

  • Contribute to both research strategy and technical implementation—this is a hands-on role. Have ownership over your own applied research stream.

  • Investigate topics such as 3D reasoning, geometric representations, multimodal reasoning, world models, simulation, and physical intuition.

  • Stay on the edge of what’s possible and bring promising ideas into reality (e.g. following the literature, attending conferences, etc).

About you:

  • Have conducted research in one or more areas such as spatial reasoning, geometric deep learning, 3D vision, multimodal reasoning, world models, robotics, simulation, or related fields.

  • Are fluent in Python and modern ML stack such as PyTorch or JAX, and distributed training frameworks.

  • Creative approach to bringing definition and solutions to under-specificed challenges.

  • Are fascinated by how intelligent systems perceive, represent, and reason about the physical world.

  • Thrive in fast-moving, collaborative environments, and communicate technical matters with clarity.

  • Take a high-ownership, “make it work” approach to problem solving.

As a bonus, you may have:

  • A PhD (or equivalent experience) in Computer Science, Robotics, Engineering, Math, or a related field.

  • Publications in top-tier venues.

  • Experience working with CAD data, point clouds, meshes, simulation environments, or other geometric representations.

Location:

Onsite in San Mateo, CA. Relocation support available.

Benefits:

Competitive salary, meaningful equity ownership, healthcare, dental, vision, 401(k) match, and unlimited PTO.

Interview process:

  • Initial screening call (30 mins)

  • Biographical/behavioural interview (60 mins)

  • Technical interview (75 mins)

  • CEO interview (30 mins)

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