ML Engineer, Platform

The Team

Software Engineering at Pi builds the systems that scale research breakthroughs into reliable products in the physical world.

This role sits at the intersection of our AI, infrastructure, and external partners. You’ll work closely with researchers and engineers to turn rapidly evolving model capabilities into APIs and tools that external users, from hobbyists to large organizations, can actually build on.

Today, onboarding a new partner to Pi can involve significant hands-on work. The goal is to make that increasingly unnecessary: partners should be able to bring us data, fine-tune a model, evaluate it, and run inference through a product that is intuitive, reliable, and built to scale.

In This Role You Will

This is a software and systems role first. Robotics experience is not required, and we’re particularly excited about people who have built developer platforms, model APIs, or infrastructure products outside of robotics.

• Build Pi’s model API end to end, including data ingestion, fine-tuning, evaluation, low-latency remote inference, partner-facing tools, and deployment integrations.
• Design systems that can scale from a small number of deeply integrated partners to thousands of organizations and potentially millions of robots.
• Architect reliable, multi-tenant infrastructure around rate limiting, isolation, backpressure, versioning, observability, and SLOs.
• Turn partner data into a first-class product experience, from initial upload through validation, processing, fine-tuning, and evaluation.
• Build and operate low-latency inference systems that allow Pi models to control robots running in real-world environments.
• Work closely with researchers to turn new model capabilities into stable, usable product surfaces.
• Learn from how partners use the platform and turn recurring friction into better APIs, tooling, documentation, and abstractions.
• Write high-quality production code that integrates deeply with Pi’s existing infrastructure.
• Define what the developer platform for general-purpose robotics should look like. There is no established playbook for this yet.

What We Hope You'll Bring

• Strong software engineering fundamentals and experience building production systems.
• Deep backend and systems experience across APIs, services, databases, caching, distributed systems, and infrastructure.
• Experience building and scaling developer platforms. We’re especially interested in people who have shipped platforms for model fine-tuning, inference, or other compute-intensive workloads.
• An understanding of the problems that emerge as systems scale: reliability, latency, multi-tenancy, versioning, observability, and operational complexity.
• Enough familiarity with machine learning systems to deploy, serve, and debug models in production. You do not need to be an ML researcher.
• Strong Python skills and the ability to work comfortably across infrastructure and product boundaries.
• A high degree of ownership. You’re comfortable taking an ambiguous problem, building the first version end to end, and then designing the system so it no longer depends on you.

Bonus Points If You Have

• Experience building low-latency or real-time systems, including streaming, inference transport, WebSockets, QUIC, or similar technologies.
• Experience building model-serving, inference, fine-tuning, or developer-platform infrastructure.
• Experience at an early-stage infrastructure, AI, robotics, or autonomous systems company.
• Familiarity with our stack: Python, Postgres, ClickHouse, GCP, Kubernetes, Modal, React, and TypeScript.
• Experience with security, authentication, authorization, or multi-tenant infrastructure.

What This Role Is Not

• This is not a Customer Success role. You won’t own ongoing partner support or change management inside customer organizations.
• This is not a Partner Roboticist or Applied Research role. You won’t be responsible for training policies for individual partners or debugging robots on site.
• This is not a traditional ML infrastructure role focused primarily on training systems, compilers, or accelerator scheduling. You’ll work closely with those systems, but your focus is the product and platform through which the outside world uses Pi’s models.