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About the Role

We are building a Large Physics foundation Model (LPM) to achieve general causal intelligence, enabling AI to predict and alter physical systems, starting with weather. Your mission is to own the path from trained model to customer value, building production systems, customer-facing surfaces, and demos that turn research into a product.

Responsibilities

• Build and operate production systems delivering model predictions to customers under hard real-time deadlines, ensuring reliability, cost efficiency, monitoring, alerting, and incident response.
• Design and build the full product surface, including backend APIs, data delivery, integration patterns, and frontend dashboards/visualizations for actionable predictions.
• Own the packaging, security, observability, and upgrade machinery for deploying the product into diverse customer environments (cloud, VPC, on-prem, restricted networks).
• Create product demos and prototypes with and for prospective customers, iterating rapidly with the go-to-market team.
• Work directly in customer environments to integrate with their data and systems, ship on-site solutions, and translate learnings into research and product requirements.
• Design tooling and playbooks to generalize solutions across different customers.

Requirements

• 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 associated systems.
• Ability to own deliverables end-to-end, from requirements through autonomous execution.
• Relentless approach to problem-solving, rapid execution, and ability to quickly learn in unfamiliar domains.

Skills

• Backend systems
• APIs
• Cloud infrastructure (GCP, AWS, or Azure)
• Modern frontend frameworks
• ML systems deployment and operation
• Containerization
• Orchestration
• Infrastructure-as-code (Kubernetes, Docker, Terraform)
• Customer interaction and problem scoping
• Scalable model serving & deployment architectures

About the Company

• Our mission is general causal intelligence; AI that is capable of (1) predicting the future and (2) identifying the actions to alter it.
• 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.
• Our founding team has built and deployed AI against the physical world in robotics, drug discovery, and particle physics at institutions like DeepMind, Waymo, Cruise, Insitro, Nabla Bio, and CERN.