Physical Superintelligence
AI systems, computational science, software engineering, physics
MemberofTechnicalStaff,Infrastructure
Neural analysis suggests this role is
optimal for Mid candidates.
“Member of Technical Staff, Infrastructure at Physical Superintelligence. Skills: Platform infrastructure, Cloud foundations, CI/CD pipelines, Production deployments, Multi-cloud infrastructure, Infrastructure-as-code, Release engineering, MLOps, Operational excellence. 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”
What You'll Achieve.
Discover and commercialize transformative physics breakthroughs at scale with artificial superintelligence, safely, verifiably, and for broad public benefit; Ship code from commit to production; Fast, safe deploys; Meet SLOs; Build systems that scale without bureaucracy
Industry & Context.
Systems thinking; Incident response; Reducing toil with code
In-person role, On-call
What They're Looking For.
Must Have
Four or more years operating cloud infrastructure in production at companies known for engineering rigor (e. g. , Stripe, Cloudflare, Datadog, Snowflake, Databricks, Google, Netflix, or comparable), at multi-cloud scale, Written code and shipped infrastructure that paying customers, internal teams, or large user bases depend on every day, Deep fluency with infrastructure as code (Terraform, Pulumi, or comparable), Deep fluency with CI/CD systems, Deep fluency with Kubernetes, Deep fluency with major cloud platforms (GCP and AWS at minimum), Built and operated multi-cloud production deployments end-to-end, from initial cloud setup through to release pipelines, Machine learning and training-workload operations experience, GPU scheduling, Distributed training infrastructure, Model-serving pipelines, Observability for ML systems, Run production training jobs and shipped served-model surfaces, Operational excellence and on-call discipline, Led incidents, Written runbooks, Reduced toil with code, Built systems that scale without bureaucracy, 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 with OpenTelemetry, Prometheus, Grafana, or comparable, Background in scientific computing, HPC, or research compute environments
What You'll Do.
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
and adjacent providers
Establish the infrastructure-as-code discipline at PSI: choose the tooling
and make every research workflow
and customer-facing AI product deployable through code
Design and run the release engineering pipeline that ships code from commit to production
Operate the production infrastructure that powers our AI platform at scale: the paid API
model training jobs for our proprietary physics LLM
agentic research workflows
and customer deployments
build observability and alerting
schedule GPU and CPU capacity
lead incident response
Be the leverage layer for the rest of engineering
not tickets they wait on
How You'll Work.
Team & Collaboration
Platform engineers depend on you; Product engineers depend on you; Security engineers depend on you; Research engineers depend on you
Full Job Description
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 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. Every change flows through automated tests, security scans, and progressive rollouts. Fast, safe deploys are the default; long manual release cycles are not. Operate the production infrastructure that powers our AI platform at scale: 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, lead incident response. Be the leverage layer for the rest of engineering. Platform, product, security, and research engineers all depend on you for reliable cloud primitives,
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