PSI is a well-funded stealth startup with roots at Harvard, MIT, and other leading global research institutions. Our mission is to build AI systems that discover new physics at scale. To bridge the gap between our core discovery platform and the complex needs of our early partners and research teams, we are seeking a Solutions Architect.
This individual will be the technical bridge between our frontier AI-driven physics discovery platform and the real-world implementation requirements of external customers and research organizations.
As a Solutions Architect, you will design and oversee the implementation of integrated environments that allow our AI systems to thrive within diverse technical ecosystems.
Architect Integrated Discovery Pipelines: Design end-to-end architectures that connect domain-specific scientific software with our machine learning discovery workflows.
Customer-Centric Infrastructure Design: Work with external customers and research teams to build production-grade web applications, backend services, and APIs tailored to their specific scientific needs.
Scalable Deployment Strategy: Create containerized architectures and orchestration systems (Kubernetes/Docker) that support large-scale extended simulations while ensuring computational efficiency.
Technical Consultation & Evangelism: Act as the primary technical point of contact for external partners, helping them navigate GPU scheduling, compute resource management, and cloud infrastructure (AWS/GCP/Azure).
Workflow Optimization: Implement CI/CD pipelines and infrastructure-as-code (Terraform) to ensure partner environments are reproducible, versioned, and optimized for experiment tracking.
We seek a candidate who possesses the rare combination of high-level systems thinking and the ability to "roll up their sleeves" and code. You should have a track record of building production systems that technical users love.
Technical Depth Requirements:
Full-Stack & Systems Fluency: Proficiency in Python, Go, or C++, paired with a strong understanding of modern web frameworks like React, TypeScript, and Next.js.
Data & API Design: Deep experience with PostgreSQL, Redis, and designing robust REST or GraphQL APIs to serve complex data systems.
ML & Scientific Infrastructure: Familiarity with PyTorch or JAX, and experience using orchestration frameworks like Ray, Airflow, or Argo to manage machine learning workloads.
Cloud & DevOps Mastery: Expertise in Docker, Kubernetes, Terraform, and monitoring tools such as Prometheus and Grafana.
Scientific Curiosity: A background or strong interest in high-performance computing (HPC) and physics simulations.
Location: This is an in-person role based in our Boston or San Francisco offices.
Compensation: We offer a competitive package including salary, comprehensive benefits, and meaningful early-stage equity.
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