Member of Technical Staff — Product Engineering

To achieve this breakthrough, we are building a Large Physics foundation Model (LPM) because physical systems, unlike text or images, are governed by verifiable cause and effect. We believe that scaling on physics will enable an understanding of causality required to predict and control physical systems, starting with weather.

We look for software engineers who are excited to tackle unsolved problems. As our models reach the real world, predictions must arrive reliably, on hard deadlines, in whatever environment our customers operate. Your mission is to own the path from trained model to customer value — the production systems that serve predictions, the surfaces customers touch, and the demos that turn frontier research into a product, making every deployment easier than the one before it.

Responsibilities

• Build and operate the production systems that deliver model predictions to customers around hard real-time deadlines — owning reliability end to end, from cost efficiency to monitoring, alerting, and incident response
• Design and build the full product surface: backend APIs and data delivery, integration patterns, and frontend dashboards and visualizations that make predictions actionable
• Own the packaging, security, observability, and upgrade machinery to deploy our product into customer environments — cloud, VPC, on-prem, and restricted networks
• Create product demos and prototypes with and for prospective customers, iterating rapidly alongside go-to-market
• Work directly in customer environments when needed: integrate with their data and systems, ship solutions on-site, and translate what you learn into requirements for research and product
• Design the tooling and playbooks that let solutions built for one customer generalize to the next

What we're looking for

• Strong generalist software engineering skills across the stack: backend systems, APIs, cloud infrastructure (GCP, AWS, or Azure), and modern frontend frameworks
• Experience deploying and operating ML systems in production, ideally across diverse or customer-controlled environments
• Familiarity with containerization, orchestration, and infrastructure-as-code (e.g. Kubernetes, Docker, Terraform)
• Comfort working directly with customers: scoping ambiguous problems, building demos under time pressure, and representing the company technically
• Background in scalable model serving & deployment architectures and the systems around them
• Owns deliverables end-to-end, from requirements through autonomous execution