# Member of Technical Staff — Product Engineering at Causal > Onsite · United States · Staff · Software engineering · open. Source: https://alderhq.app/jobs/ashby%3Acausal%3A2cb5da89-e32f-4132-9980-c06bc221f2b6 (read 2026-11-08). ## Facts (observed) - Company: [Causal](https://alderhq.app/companies/causal) - Location text: San Francisco; Singapore - Where it can be done from (inferred): Onsite · United States (evidence: "Location: San Francisco; Singapore") - Posted: 2026-07-20 (111 days ago) - First seen by alder: 2026-11-04 · last verified: 2026-11-07 - Reposted: 0 times · edited: 0 times - Apply on the employer's site: https://jobs.ashbyhq.com/causal/2cb5da89-e32f-4132-9980-c06bc221f2b6 - Skills listed (inferred): AWS, GCP, Azure, Kubernetes, Docker, Terraform ## Pay and sponsorship - Posted pay: not posted - Market band for similar posted roles (SF Bay Area, n 969): p25 $205k, median $240k, p75 $284k ## Description (the employer's text) 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 --- Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/ashby%3Acausal%3A2cb5da89-e32f-4132-9980-c06bc221f2b6 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.