Fullstack Software Engineer

Physical Intelligence
San Francisco, CA

Who this role is best for

Aimed at mid-level fullstack engineers who thrive in cross-functional AI research and operations teams, with expertise in React, TypeScript, Python, and cloud infrastructure, based in San Francisco.

Best fit for

  • Candidates with experience building annotation tools and data-centric AI systems for research teams
    — “Build the platform and workflows for annotation generation
  • Individuals who can translate ambiguous research needs into executable software workflows
    — “Translate requirements from researchers into an actionable plan
  • Engineers comfortable with iterative development and scaling practical v0 solutions
    — “Ability to start with a practical v0 and build toward scalable

Things to consider

  • Requires hands-on experience with both frontend and backend production systems
    — “Ship production-quality software: Build reliable frontend interfaces, backend APIs
  • Collaboration with researchers demands adaptability to evolving requirements
    — “Experience working directly with users, iterating from feedback

How to stand out

  • Highlight projects integrating human-in-the-loop systems with AI workflows
    — “Annotation sits at the front of that chain
  • Emphasize cloud infrastructure experience with GCP and Kubernetes
    — “Experience with cloud and containerized environments such as GCP and Kubernetes
  • Showcase ability to design end-to-end tooling for research operations
    — “Redesign how researchers and prototypers turn ambiguous needs into executable work
  • Demonstrate track record of shipping reliable internal-facing software
    — “Ship production-quality software: Build reliable frontend interfaces
  • Quantify impact on annotation quality, cost, or throughput metrics
    — “Build the systems that track quality, cost, throughput, and coverage
Pace · Fast PacedCollaboration · HighAutonomy · HighDecision Impact · TeamLevel · Mid Level

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

  • own annotation tooling
  • make annotation legible
  • redesign researcher request and planning workflows
  • build research and ops-facing tooling
  • ship production-quality software
Typical background
strong full stack engineering experienceexperience with cloud and containerized environmentscomfort designing workflows involving people, models, and software

Skills & requirements

Required

Full Stack EngineeringReactTypeScriptPythonPostgreSQLClickhouseKubernetes

Preferred

Building Data Labeling PlatformsHuman-in-the-loop ToolsBuilding Internal Tools For Research, Robotics, Or Operationally-intensive ProblemsTasking SystemsInstruction ManagementSchedulingResource AllocationWorkflow Orchestration

Stack & domain

ReactTypeScriptPythonPostgreSQLClickhouseKubernetesLeadershipAttention To DetailProduct JudgmentAIRoboticsData Science

About the role

Original posting from Physical Intelligence via Ashby

Physical Intelligence is bringing general-purpose AI into the physical world. We are a group of engineers, scientists, roboticists, and company builders developing foundation models and learning algorithms to power the robots of today and the physically-actuated devices of the future.

As a Fullstack Software Engineer, you will build internal products that help Pi's research and operations teams move faster. You will work closely with researchers, prototypers, operators, and other engineers to turn operational workflows into reliable software.

The Team

Fullstack builds the internal software the rest of Pi runs on. Our users span the path from research intent to physical execution: researchers designing experiments and requesting data, Research Ops working across feasibility, task design, environments, prototyping, and instructions, and Production Ops running data collection, evals, and deployments across our lab, warehouse, and real-world deployments.

Annotation sits at the front of that chain, determining what our models learn from the data we collect, and this is where this role will start.

In This Role You Will

  • Own annotation tooling: Build the platform and workflows for annotation generation, from the interfaces annotators work in to the pipelines behind them, and translate requirements from researchers into an actionable plan and the software to execute it.
  • Make annotation legible: Build the systems that track quality, cost, throughput, and coverage so researchers can see what they are getting and decide what to change.
  • Own researcher request and planning workflows: Redesign how researchers and prototypers turn ambiguous research needs into executable work, and create the software layer for understanding capacity and making tradeoffs across competing priorities.
  • Build research and ops-facing tooling: Dataset browsing, eval dashboards, and the throughput, quality, and progress tracking Production Ops needs across our lab, warehouse, and real-world sites.
  • Act as your own PM: Gather requirements, prioritize work, define success metrics, write specs, ship tools, drive adoption, and iterate based on feedback.
  • Ship production-quality software: Build reliable frontend interfaces, backend APIs, data models, dashboards, and cloud services. You should be comfortable shipping and supporting production-grade services.

What We Hope You'll Bring

  • Strong full stack engineering experience building production web applications and APIs, especially with React, TypeScript, and Python.
  • Experience with relational databases (we use Postgres), analytical systems (ClickHouse), and queueing systems.
  • Familiarity with cloud and containerized environments such as GCP and Kubernetes.
  • Comfort designing workflows where people, models, and software have to function as one system.
  • Strong product judgment and attention to detail.
  • Experience working directly with users, iterating from feedback, and navigating ambiguous workflows and evolving requirements.
  • Ability to start with a practical v0 and build toward scalable, production-quality systems.

Bonus Points

  • Former founder, early employee, or other demonstration of comfort with ambiguity and solving hard problems end to end.
  • Experience building data labeling platforms, human-in-the-loop tools, or other data-centric AI systems.
  • Experience building internal tools specifically for research, robotics, or operationally-intensive problems.
  • Experience with tasking systems, instruction management, scheduling, resource allocation, or workflow orchestration.
  • Experience with our specific stack: React, TypeScript, Python, Postgres, ClickHouse, GCP, and Kubernetes.

Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

Source: Physical Intelligence careers (Ashby)

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