Location: San Mateo, CA (In-Person, Bay Area)

About Primepoint

Primepoint is building construction intelligence for the physical world. We transform massive, static construction datasets (drawings, specifications, revisions, submittals) into an interactive, AI-powered system that surfaces conflicts, answers complex questions, and reduces schedule and cost risk on $100M–$3B+ building projects.

We go beyond LLM wrappers. Our mission requires deep multimodal reasoning, document understanding, and applied computer vision at production scale.

Our founders built Facebook’s first computer vision team and helped launch Facebook AI Research, and also founded startups in neural video compression (acquired by Apple).

We are a technical team backed with $10M in seed funding and already working with paying customers spanning major hospital and large-scale commercial projects.

We are in-person in San Mateo because we believe tight collaboration accelerates product velocity.

The Role

We’re hiring a Full Stack Engineer who can build end-to-end product features in a highly ambiguous, high-ownership startup environment.

You will:

• Own features from concept to deployment

• Build complex, high-performance front-end systems (React)

• Develop backend services (Python + Postgres)

• Work on document intelligence, search, change detection, and large-scale data processing

• Ship fast and iterate directly with customers

• Collaborate closely with founders on product direction

This is not a “small-slice” big tech role. You will build real systems that customers use on billion-dollar construction projects.

What We’re Looking For

Must-Have

• Strong full stack engineering ability (Python + Postgres + React)

• Proven builder: shipped meaningful systems end-to-end

• Comfortable operating with ambiguity and minimal hand-holding

• Evidence of high performance (selective company, fast promotions, or exceptional output)

• Embraces modern AI tooling (LLMs, code assistants, etc.)

• Based in the Bay Area and willing to work in-person

Ideal, but not required

• Experience with PDF processing, document parsing, or complex data pipelines

• Distributed systems or search/indexing experience

• Product-engineer mindset

• Startup experience (founding or early-stage preferred)

• Exposure to applied machine learning

Interview Process

• Quick Recruiter chat

• 30-min Hiring manager conversation

• Technical interview

• Onsite collaborative problem solving session (LLM use encouraged)