Superintelligence

Machine Learning Engineer

• 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.

Member of Technical Staff, ML Engineer

Artificial Intelligence

• 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.
• We are seeking a Head of Applied AI to build and lead the organization that applies PSI's AI and physics capability to real commercial problems.
• Role and Responsibilities
• - Applied AI is the organization responsible for putting PSI's AI and physics capability to work on real commercial problems: engagements where a customer's operational or engineering problem gets solved with our models, simulations, and agents. This is applied science aimed at revenue.
• - Build the organization itself. Hire the founding team, define how it works, and own its results.
• - Own customer delivery end-to-end. Scope each engagement around the customer's actual problem, build the models and simulations that solve it, and ship something that runs in their environment. Every engagement either compounds a reusable technical capability or is killed at renewal.
• - Own the interfaces through which researchers and customers put PSI's AI and physics work to use. These exist to make the underlying science usable and trustworthy.
• - Partner with go-to-market on scoping and expansion: be the technical counterpart to sales, decide what we can credibly promise, and serve as the escalation point when an engagement is at risk.
• - Stay technical. This is a player-coach role: carry individual technical work on engagements, and set the bar for what shipped means by shipping working science.
• - Pull research from Core AI and domain expertise from Physics into what you ship, and build on the infrastructure Engineering runs. You are the team that turns the rest of the company's work into something a customer pays for, so these partnerships are constant.
• What We're Looking For
• - Eight or more years applying ML and simulation to solve real-world commercial or engineering problems, with at least three leading customer-facing technical teams (solutions engineering, forward-deployed engineering, or applied AI) at a company known for technical rigor. You have run engagements where the customer's problem set the agenda.
• - A track record of building a function: you have hired and led a customer-facing technical team from small or from scratch, and the function outlived your direct involvement in every deal.
• - Strong applied-ML and computational grounding. You can build a model, debug a pipeline, and reason about accuracy, speed, and extrapolation risk trade-offs with a senior domain engineer. Physics or engineering literacy in at least one quantitative domain.
• - Experience owning the tools or interfaces through which technical work reaches its users, as a means to getting real science into customers' hands.
• - Motivation for this specific job. This role is commercial problem-solving with real science. We will assess for wanting exactly this.
• Nice to Have
• - Experience with digital twins, physics simulation (OpenFOAM, Ansys, COMSOL, or comparable), or ML for engineering domains.
• - Domain experience in energy, infrastructure, or other industrial verticals.
• - Forward-deployed or solutions leadership at a company that scaled the function significantly.
• - PhD or master's in a quantitative discipline.
• 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 is an in-person role based in Boston. We offer competitive compensation including salary, benefits, and meaningful early-stage equity. We evaluate on delivery track record, technical depth, applied scientific judgment, and the quality of the teams you have built. We are an equal opportunity employer and value diverse perspectives in building platforms for AI-driven discovery.