Member of Technical Staff

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. We are seeking engineers to build platform infrastructure at the intersection of computational science, AI systems, and software engineering.

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, Performance & Capacity to answer two questions honestly: how much compute do we actually need, and how well are we using what we have. Your job is to make both answers measurements rather than estimates, and then be held to them. Then make the same fleet produce more, quarter after quarter.

Responsibilities

• Measure the fleet instead of estimating it. Utilization, goodput, and training efficiency, taken on real workloads, so that capacity decisions rest on numbers someone actually observed. Where the platform wastes capacity, you find it and you close it.

• Own the performance of research workflows end to end. A campaign's time is spent in kernels, collectives, the scheduler's placement decisions, the filesystem, and the queue. When it runs slow, you find which layer, and you fix what lives at the systems level or hand the owning team a diagnosis sharp enough to act on. The unit you optimize is the workflow, not the box.

• Make the scheduler earn its fleet. The orchestration systems belong to the Distributed Systems team; their efficiency is yours: placement and bin-packing quality, preemption policy, the preemptible fraction as a measured quantity, and checkpointing economics. A workload that can yield on demand is cheaper to run, and worth money in a negotiation. You argue every policy change with a before and an after.

• Own the decisions. The capacity model that turns a research workload into a defensible node count with stated assumptions and error bars, validated against observed demand. Which GPUs we buy, and what we ask providers for. Which serving engine the inference fleet runs, and which hardware a workload lands on. Yours to make, defended with measurements, and held to when real money is committed.

What We're Looking For

• Five or more years with GPU and large-scale compute workloads, including real multi-node experience: distributed training performance, interconnect and collective-communication behavior, and the patience to find where scale breaks down. Single-box optimization is not this job.

• You know GPU performance characteristics at the system level: which workloads justify an H100 and which a B200, how to optimize across clusters rather than within one, and where the bottleneck actually sits. Systems view first, the weeds when the numbers demand it.

• You understand AI training and inference performance deeply. On training: where step time goes, what MFU means and why it disappoints, how communication hides behind compute or fails to. On inference: why decode is bandwidth-bound, what batching buys, and what actually sets the latency floor. You can find the bottleneck in either regime and say what it costs.

• You work in a loop: form a model of where the time should go, instrument the system, find where it actually went, change something, and verify the win on a real workload. You work from profilers, traces, and metrics rather than folklore, and you can instrument a system you did not write. The depth this job needs is judgment about where time and money go: you implement fixes at the systems level yourself, and when the root cause belongs in a specialized compiler, kernel, storage, or networking component, you produce a diagnosis sharp enough for its owner to act on.

• You have owned a capacity decision with money attached and been held to the number. You can walk us through the assumption that turned out wrong and what you changed.

• You can explain a performance result to a researcher without making them learn the internals. Your numbers have to be usable by people who will never open a profiler.

Nice to Have

• Time on a data center floor: thermal and power envelopes, physical failure domains, capacity planning against real hardware rather than a console.

• Energy- or preemption-aware scheduling. You have treated power, time of day, hardware availability, or interruptible capacity as real variables in where work lands.

• Capacity sourcing across cloud and specialist GPU providers, and the economics of moving workloads between them.

• You have operated or benchmarked modern inference engines (vLLM, SGLang, or comparable) and can say when each earns its place.

• You have built or substantially contributed to a scheduler, runtime, or communication library. This role optimizes those systems rather than owning them, and having built one makes the diagnosis sharper.

• Background in scientific computing or HPC environments, including large-scale simulation codes and irregular workloads, where efficiency was measured because someone paid for the machine.

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, scientific curiosity, and shipping velocity. We are an equal opportunity employer and value diverse perspectives in building platforms for AI-driven discovery.