Research Engineer (Agentic systems, AI, Full-Stack)

Physical Superintelligence•San Jose, CA

3d•Onsite

About The Position

Physical Superintelligence is a stealth startup with roots at Google, 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.

Requirements

Fluency in scientific computing concepts, modern software engineering practices, machine learning infrastructure, and production systems design.
Track record building production systems that technical users adopt.
Strong fundamentals across software engineering, computational methods, and infrastructure.
Depth in at least two to three relevant technical areas and the ability to work across the full stack from scientific computing to production deployment.
Python, or similar systems languages.
Full-stack development using React, TypeScript, Next.js, and modern web frameworks.
Backend services, REST and GraphQL APIs, data systems including PostgreSQL and Redis, and real-time systems.
Docker, Kubernetes, container orchestration, cloud platforms including AWS, GCP, or Azure, and infrastructure as code using Terraform.
CI/CD pipelines, monitoring with Prometheus and Grafana, GPU scheduling, and compute resource management.
PyTorch, JAX, or similar frameworks with experiment tracking systems such as MLflow or Weights & Biases.
Orchestration frameworks including Ray, Airflow, or Argo, and distributed training infrastructure.
High-performance computing environments, physics simulations or domain-specific scientific software.
Building tools at AI labs, machine learning-focused startups, or research organizations.

Responsibilities

Develop a complex agentic system to support emerging superintelligence, with a focus on solving challenges in physics.
Work across computational science simulation, AI systems, full-stack development, and infrastructure to build the platform enabling AI-driven physics discovery.
Design production-ready systems, including security considerations.
Build infrastructure supporting model training, evaluation, and deployment with experiment tracking, versioning, and reproducibility systems.
Implement orchestration for machine learning workloads across cloud infrastructure and develop instrumentation for understanding agent behavior and scaling.
Build production web applications serving research teams and external customers with responsive interfaces, backend services, and APIs.
Create containerized architectures and orchestration systems with CI/CD pipelines, infrastructure as code, GPU scheduling, and compute resource management.