Member of Technical Staff, Infrastructure

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. 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. Responsibilities Own the full infrastructure stack end‑to‑end, from cloud foundations through CI/CD pipelines to production deployments. Build and operate multi‑cloud infrastructure for our AI platform across GCP, AWS, and adjacent providers. Establish the infrastructure‑as‑code discipline at PSI: choose the tooling, design the modules, and make every research workflow, training job, and customer‑facing AI product deployable through code. Design and run the release engineering pipeline that ships code from commit to production through automated tests, security scans, and progressive rollouts. Operate the production infrastructure that powers our AI platform at scale—including the paid API, model training jobs for our proprietary physics LLM, agentic research workflows, and customer deployments. Define and meet SLOs, build observability and alerting, schedule GPU and CPU capacity, and lead incident response. Serve as the leverage layer for the rest of engineering: platform, product, security, and research engineers depend on you for reliable cloud primitives, fast deploys, and visible production behavior. Write tools they use, not tickets they wait on. Qualifications Four or more years operating cloud infrastructure in production at companies known for engineering rigor (e.g., Stripe, Cloudflare, Datadog, Snowflake, Databricks, Google, Netflix). Deep fluency with infrastructure‑as‑code (Terraform, Pulumi, or comparable), CI/CD systems, Kubernetes, and major cloud platforms (GCP and AWS at minimum). Experience with machine learning and training‑workload operations—GPU scheduling, distributed training infrastructure, model‑serving pipelines, observability for ML systems. Comprehensive operational excellence and on‑call discipline—led incidents, written runbooks, reduced toil, built scalable systems. Favor self‑service abstractions over tickets and visibility over heroics. Nice to Have Built CI/CD or release engineering pipelines from scratch at a fast‑growing company. Hands‑on with model serving infrastructure such as vLLM, Triton, or comparable. Production observability experience with OpenTelemetry, Prometheus, Grafana, or comparable. Background in scientific computing, HPC, or research compute environments. 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. #J-18808-Ljbffr Physical Superintelligence