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Member of Technical Staff — Research Engineering, Evaluation

Causal · San Francisco

onsitestaffPosted Jul 20, 2026

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About this role

## Mission We’re building **general causal intelligence**—AI that can both **predict the future** and **identify actions to alter it**. To do this, we’re developing a **Large Physics Foundation Model (LPM)**, grounded in the idea that physical systems have **verifiable cause and effect**. We’re starting with **weather**, and our founding team has built and deployed AI in the physical world across **robotics, drug discovery, and particle physics** (e.g., DeepMind, Waymo, Cruise, Insitro, Nabla Bio, CERN). ## Role: Member of Technical Staff — Research Engineering, Evaluation Your mission is to build the **central evaluation framework** that the entire research organization runs on—covering **pipelines, metrics, and tools** that turn results into shared, trustworthy understanding. ## Responsibilities - Design and build a **central, reusable evaluation framework** used by every model and team - Implement **evaluation pipelines**, **benchmark suites**, and **baselines** to measure and compare model quality across efforts - Build **visualization and dashboard tools** that convert raw results into shared, actionable insights - Establish **sound statistical methodology** so teams can distinguish real improvements from noise - Partner with research and domain teams to translate what “good” means into **standardized, automated metrics** ## What We’re Looking For - A relentless approach to problem-solving, **rapid execution**, and fast learning in unfamiliar domains - Strong software engineering skills; experience building **data/evaluation pipelines at scale** - Experience turning research/model outputs into **metrics, benchmarks, and visualizations** teams rely on - Solid grasp of **probability and statistics**, with judgment to design evaluations that measure what they claim - **Full-stack** capability: comfortable building both backend pipelines and frontend tools people use to interpret results - Ability to **own deliverables end-to-end**, from requirements through autonomous execution

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