Member of Technical Staff — ML Research, Planning
Causal · San Francisco
About this role
## Mission We’re building **general causal intelligence**—AI that can: 1) **Predict the future**, and 2) **Identify the actions** that can alter it. To do this, we’re developing a **Large Physics foundation Model (LPM)**. Unlike text or images, physical systems are governed by **verifiable cause and effect**. We believe scaling on physics will unlock the **causality** needed 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 including **DeepMind, Waymo, Cruise, Insitro, Nabla Bio, and CERN**. ## Role: Planning Layer for Interventional Causality Predicting the future is only half the battle. The other half is identifying the actions that can change it. Your mission is to build the **planning layer** on top of the LPM by: - Conditioning the model on **objectives** - Producing **actions** that achieve them This spans **operational decisions** through to **physical interventions**, enabling **interventional causality** (not just observational causality). There’s no established playbook—this is a chance to define it. ## Responsibilities - Research and implement methods that turn a predictive physics model into one that reasons toward objectives: - **planning, control, decision-making** against a learned model of the world - Develop approaches for **decision-making under uncertainty** in **high-dimensional, continuous** physical state spaces - Build interfaces for specifying **objectives and constraints**, and methods for producing actions that satisfy them - Run **experiments and ablations** connecting reasoning methods to **decision quality** - Work across the full ML stack: - **data, model, eval, and infrastructure** - take ideas from **prototype** to **scaled training runs** ## 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 grasp of **machine learning fundamentals**, with depth in at least one relevant area (e.g. **reinforcement learning, planning and control, decision-making under uncertainty, model-based RL, post-training of large models**) - Experience training models and understanding experimental results through careful **analysis and ablation studies** - Familiarity with the challenges of **reasoning, planning, or acting** with learned models - A track record of turning open-ended research problems into **working systems**
Listing freshness
CronJobs last confirmed this listing 2h ago. If its source stops confirming the opening for seven days, this page is removed from active inventory.