---
title: 'Member of Technical Staff, ML Engineer at Physical Superintelligence'
canonical: 'https://feeny.ai/job/member-of-technical-staff-ml-engineer-physical-superintelligence-boston-zbj6gms6dqrn'
type: 'job'
last_seen: '2026-09-09'
---

# Member of Technical Staff, ML Engineer at Physical Superintelligence

- **Company:** Physical Superintelligence
- **Location:** Boston, MA
- **Employment:** full-time
- **Work type:** hybrid
- **Posted:** 2026-08-01
- **Last confirmed live:** 2026-09-09
- **Apply:** https://jobs.ashbyhq.com/psi/28b392c6-422e-4bfe-aa97-0ba379f91c55

## Job description

## OVERVIEW

Physical Superintelligence is a startup with roots at Google, NVIDIA, Harvard, Meta, MIT, Oxford, Johns Hopkins, Cambridge, and the Perimeter Institute building AI systems to discover new physics at scale.

Our mission is to discover and commercialize transformative physics breakthroughs at scale with artificial superintelligence, safely, verifiably, and for broad public benefit.

The last century's golden age of physics gave us transistors, lasers, and nuclear energy. We believe artificial superintelligence will unlock the next one. We're creating the infrastructure to industrialize scientific discovery and usher in this new era.

We have one product: new physics, at scale.

## ROLE

We are seeking a Member of Technical Staff, ML Engineer to build and run the training and inference systems that turn Core AI's research into things that work at scale, and that the rest of Engineering can build on.

## RESPONSIBILITIES

- Own the training and inference infrastructure that Core AI depends on: distributed training jobs, GPU scheduling, and model-serving systems (vLLM, SGLang, or comparable) for both proprietary models and self-hosted inference.
- Build the tools and abstractions AI researchers use to launch training runs, iterate on inference providers, and route workloads across models, so a researcher's time goes into the science instead of the plumbing.
- Partner with Engineering on the shared platform: capacity planning, observability, and reliability for GPU and inference infrastructure, so training and serving hold up to the same production bar as everything else we ship.
- Debug and harden the training and inference stack under real load. Egress failures, stalled retries, and routing edge cases are your problem to close, not someone else's ticket.
- Stay hands-on. You write the code, not just the design doc, and you are the first call when a training job stalls or an inference path breaks.

## WHAT WE'RE LOOKING FOR

- Three or more years building and operating ML training or inference infrastructure in production, at a company that trains or serves models at meaningful scale.
- Hands-on experience with distributed training (multi-GPU or multi-node, using PyTorch, Ray, or comparable) and model-serving systems (vLLM, SGLang, Triton, or comparable).
- Strong software engineering fundamentals. You can build a service that other engineers and researchers depend on every day, not a script that worked once.
- Enough ML fluency to work productively with AI researchers: you understand training loops, reward signals, and inference-time behavior well enough to debug them, even without designing the algorithms yourself.

## NICE TO HAVE

- Experience building internal platform tools such as training-as-a-service APIs, inference gateways, or job schedulers.
- Background in GPU infrastructure, CUDA, or performance engineering for ML workloads.
- Experience with cloud infrastructure (GCP, AWS) and infrastructure as code (Terraform or comparable).
- Prior work embedded alongside a research team, turning research code into production systems.

## HOW WE WORK

We hold a high technical bar and give people full ownership of their work, from spec to ship to on-call. We write contracts before logic, test against real systems instead of mocks, and favor simple designs that ship over clever ones that do not. Our development process is AI-native: we work with agentic coding tools daily, write specs that are legible to humans and agents alike, and lead with leverage.

## LOCATION AND COMPENSATION

This role is based in Boston. We will consider remote candidates on a case-by-case basis. We offer competitive compensation including salary, benefits, and meaningful early-stage equity. We evaluate on technical breadth, systems thinking, ML infrastructure depth, and shipping velocity. We are an equal opportunity employer and value diverse perspectives in building platforms for AI-driven discovery.

## About Physical Superintelligence

## Company Overview
- **One-liner**: Building the world’s first vertically integrated factory for physical superintelligence to discover and commercialize transformative physics breakthroughs at scale with artificial superintelligence.
- **Entity Type**: Private (Public Benefit Corporation)
- **Headquarters**: Boston/Cambridge, MA, USA and San Francisco, CA, USA
- **Founded**: Not publicly available (company website launched April 2026)
- **Founders**: Dr. Alex Wissner-Gross and Matthew Pines (co-founders) [theinnermostloop.substack.com](https://theinnermostloop.substack.com/p/physical-superintelligence)

## Core Business
- Primary industry/industries: Artificial Intelligence, Physics R&D, Scientific Discovery
- Target customers: B2B – organizations with hard physics problems (e.g., energy, materials, defense); also open-source community for its AI copilot.
- Mission or purpose statement: “Discover and commercialize transformative physics breakthroughs at scale with artificial superintelligence – safely, verifiably, and for broad public benefit.” [psi.inc](https://www.psi.inc/)

## Products & Services
- **[Get Physics Done (GPD)]**: An open-source AI copilot built by physicists for physicists – the first agentic AI physicist. Released as open-source software (GitHub stars: 835). [github.com](https://github.com/psi-oss)
- **Vertical Integration Platform**: PSI is building an end-to-end system that reasons like a theorist, validates like a computational physicist, tests like an experimentalist, and drives breakthroughs through to commercial deployment. [psi.inc](https://www.psi.inc/)

## Market Standing
- **Valuation/Market Cap**: Not publicly available
- **Key Metric**: Open-source repository “Get Physics Done” has 835 GitHub stars; team size ~20 employees (physicists, engineers, AI researchers) [theinnermostloop.substack.com](https://theinnermostloop.substack.com/p/physical-superintelligence)
- **Notable Investors/Partners**: Not disclosed. Board includes Dr. Alex Wissner-Gross (co-founder). Selectively partnering with organizations facing hard physics problems. [theinnermostloop.substack.com](https://theinnermostloop.substack.com/p/physical-superintelligence)
- **Growth Signals**: Actively hiring for multiple roles (AI research, engineering, physics); open-sourcing internal tools; expanding team in Boston and San Francisco. [jobs.ashbyhq.com](https://jobs.ashbyhq.com/psi)

## Competitive Advantages
- **First-mover in physical superintelligence**: Vertically integrated approach from theory to commercial deployment, unlike pure AI labs or traditional R&D.
- **World-class talent pool**: Team from Google, OpenAI, Meta, Harvard, MIT, Stanford, Oxford, Cambridge, Johns Hopkins, Perimeter Institute [theinnermostloop.substack.com](https://theinnermostloop.substack.com/p/physical-superintelligence).
- **Public Benefit Corporation (PBC)**: Governance structure designed for broad public benefit from day one, enabling long-term, mission-aligned work.
- **Open-source strategy**: GPD lowers barriers for physicists and builds community trust and adoption.

## Strategic Focus
- **Industrialize scientific discovery**: Accelerate physics from a field of incremental progress to rapid, systematic breakthroughs.
- **Scale AI for physics**: Build infrastructure that combines AI reasoning, computational validation, and experimental testing.
- **Selective partnerships**: Work with organizations whose hardest problems demand fundamentally new physics.

## Why Work Here
- **Mission-driven**: Opportunity to work on unlocking the next golden age of physics (transistors, lasers, nuclear energy → new physics).
- **Remote/hybrid**: Hiring for Boston/Cambridge and remote roles [vanlett.com](https://vanlett.com/matthew_pines).
- **Small, high-impact team**: ~20 people from top institutions; flat, fast-moving culture.
- **Cutting-edge tech**: Build with AI superintelligence, open-source tools, and real physics experiments.
- **Public Benefit Corporation**: Work that prioritizes societal benefit alongside discovery.
- **Perks**: Not explicitly listed, but likely competitive for a well-funded AI startup.

## Sources
1. [psi.inc](https://www.psi.inc/)
2. [theinnermostloop.substack.com](https://theinnermostloop.substack.com/p/physical-superintelligence)
3. [github.com/psi-oss](https://github.com/psi-oss)
4. [jobs.ashbyhq.com/psi](https://jobs.ashbyhq.com/psi)
5. [vanlett.com/matthew_pines](https://vanlett.com/matthew_pines)

## Other roles at Physical Superintelligence

- [Technical Program Manager](https://feeny.ai/job/technical-program-manager-physical-superintelligence-boston-y458ar5w9dx6) — Boston, MA
- [Head of Talent](https://feeny.ai/job/head-of-talent-physical-superintelligence-boston-3m52pr352ynp) — Boston, MA
- [Member of Technical Staff, Product Engineering](https://feeny.ai/job/member-of-technical-staff-product-engineering-physical-superintelligence-boston-yn94e3wzsp1y) — Boston, MA
- [Member of Technical Staff, Engineering (General Application)](https://feeny.ai/job/member-of-technical-staff-engineering-general-application-physical-bsjmsjkj3b8e) — Boston, MA
- [Member of Technical Staff, Performance & Capacity](https://feeny.ai/job/member-of-technical-staff-performance-capacity-physical-superintelligence-boston-da6vtjr71gyn) — Boston, MA
- [Member of Technical Staff, Data Systems](https://feeny.ai/job/member-of-technical-staff-data-systems-physical-superintelligence-boston-1qz6aq8a03xq) — Boston, MA
- [Discovery Portfolio Manager](https://feeny.ai/job/discovery-portfolio-manager-physical-superintelligence-boston-p7mxn7scr9st) — Boston, MA
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- [Head of Core AI](https://feeny.ai/job/head-of-core-ai-physical-superintelligence-boston-5mdpq1fes7h4) — Boston, MA