Member of Technical Staff — Product Engineering
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At a glance
Job overview
Causal Labs is hiring a Member of Technical Staff — Product Engineering. Causal Labs is building a Large Physics foundation Model (LPM) to achieve general causal intelligence, capable of predicting the future and identifying actions to alter it. The team seeks software engineers excited to tackle unsolved problems, focusing on production systems that reliably deliver model predictions to customers, design product surfaces, and create demos to transform research into products.
Key focus areas include Build and operate production systems that deliver model predictions to customers, Own reliability end to end, from cost efficiency to monitoring, alerting, and incident response, and Design and build the full product surface: backend APIs and data delivery, integration patterns, and frontend dashboards and visualizations.
Important skills include Backend Systems, API, GCP, AWS, Azure, and Modern Frontend Frameworks. Preferred (not required): Monitoring, Alerting, Incident Management, and Data Delivery.
Skills & qualifications
Skills
Qualifications
Full 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
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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
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Design and build the full product surface: backend APIs and data delivery, integration patterns, and frontend dashboards and visualizations that make predictions actionable
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Own the packaging, security, observability, and upgrade machinery to deploy our product into customer environments — cloud, VPC, on-prem, and restricted networks
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Create product demos and prototypes with and for prospective customers, iterating rapidly alongside go-to-market
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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
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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.
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Strong generalist software engineering skills across the stack: backend systems, APIs, cloud infrastructure (GCP, AWS, or Azure), and modern frontend frameworks
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Experience deploying and operating ML systems in production, ideally across diverse or customer-controlled environments
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Familiarity with containerization, orchestration, and infrastructure-as-code (e.g. Kubernetes, Docker, Terraform)
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Comfort working directly with customers: scoping ambiguous problems, building demos under time pressure, and representing the company technically
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Background in scalable model serving & deployment architectures and the systems around them
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Owns deliverables end-to-end, from requirements through autonomous execution
You've read the whole posting — now see how you match it.