Member of Technical Staff, Distributed Systems

Overview Physical Superintelligence is a stealth 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 and Responsibilities Design and implement new runtime primitives for our AI platform. Each runtime encodes a programming model that researchers and engineers compose into agentic workflows for physics discovery. Example shapes include sequential pipelines and tree-search agents; we expect to add more as the science demands new patterns. Build and harden the multi-tenant durable workflow execution system that powers AI-driven physics research at scale: correctness under retries and replays, isolation between tenants, recovery from partial failures, and predictable behavior under load. Treat our AI platform as a library product. Design the programmatic interfaces that researchers and engineers across PSI extend, with clear architectural layers and explicit API contracts so that scientific workflows compose cleanly. Operate the platform that runs every research workflow and customer-facing AI product at PSI: define and meet SLOs, build instrumentation and alerting, plan capacity, lead incident response. What We're Looking For Four or more years building and operating distributed systems in production at companies known for engineering rigor (e.g., Google, Netflix, Meta, Cloudflare, Datadog, or comparable), on major cloud platforms (GCP, AWS, or Azure) with Kubernetes or comparable container orchestration. You have written code that paying customers, internal teams, or large user bases depend on every day, and you are fluent in the operational realities of cloud-native infrastructure. A track record of designing and shipping a Python library or internal framework that other engineers extend, not just consume. You think about API ergonomics, type-driven contracts, composability, and backward-compatible evolution as first-order concerns. Real experience implementing or substantially extending orchestration primitives, workflow engines, dataflow systems, or agent runtimes. You understand the subtle bugs that come from retries, replays, and non-deterministic execution. Operational excellence and architectural judgment. You favor simple systems over clever ones, instrument before you optimize, and can explain a programming-model or workflow-engine trade-off in two minutes. Nice to Have Hands-on experience with a durable workflow system such as Temporal, Cadence, Step Functions, Argo Workflows, or Airflow at scale. Designed or shipped a DSL, embedded DSL, or authoring surface that compiles to a deployable artifact. Production observability built on OpenTelemetry or comparable tooling. Background in scientific computing, HPC environments, or research infrastructure. How We Work We are engineering-led. Engineers own problems end-to-end, 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: engineers 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.