Member of Technical Staff — Product Engineering

Causal Labs; San Francisco (CA); On-site; USD 150,000 - 190,000; Full time

Job summary

Causal Labs is building a Large Physics foundation Model to enable reliable predictions and actions in real-world environments. As a software engineer, you will own the path from trained models to customer value—from production systems to the surfaces customers touch and the demos that turn frontier research into a practical product.

You will work across backend, APIs, cloud infrastructure, and frontend dashboards, delivering reliable, real-time predictions while collaborating with customers and

Qualifications

• Strong generalist software engineering across the stack.
• Experience deploying ML systems in production.

Responsibilities

• Build and operate production systems delivering model predictions within real-time deadlines.
• Design and build the product surface: APIs, data delivery, dashboards.
• Own packaging, security, observability, and upgrade machinery for customer environments.
• Create demos and prototypes with and for prospective customers.
• Work directly in customer environments: integrate data and ship on-site.
• Design tooling/playbooks for generalizing solutions.

Skills

Backend APIs

Cloud infra

Frontend frameworks

Customer demos

Model serving

End-to-end ownership

Tools

Kubernetes

Docker

Terraform

CI/CD

Job description

Our mission is general causal intelligence; AI that is capable of (1) predicting the future and (2) identifying the actions to alter it.

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.

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.

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

We value a relentless approach to problem-solving, rapid execution, and the ability to quickly learn in unfamiliar domains.

• 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