Member of Technical Staff — Research Engineering, Evaluation

Causal Labs; San Francisco (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Causal Labs in San Francisco is building a central evaluation framework for a Large Physics foundation Model, enabling measurable, comparable progress across teams.

You will design pipelines, benchmarks, and visualizations, and establish statistical methodologies to separate true improvements from noise, collaborating with research and domain teams.

The role emphasizes end-to-end ownership and rapid execution in a hands-on engineering culture.

Qualifications

• Proven experience building data or evaluation pipelines at scale.
• Ability translate research outputs into metrics and visualizations that teams rely on.
• Strong grasp of probability and statistics.
• Experience building backend and frontend tooling.
• End-to-end ownership of deliverables.

Responsibilities

• Design a central evaluation framework used by all teams.
• Implement evaluation pipelines, benchmarks, and baselines.
• Create visualization and dashboard tools for interpreting results.
• Establish sound statistical methods to distinguish improvements from noise.
• Collaborate with research and domain teams to define domain-specific metrics.

Skills

Software engineering

Data/evaluation pipelines

Metrics/benchmarks/visualizations

Statistics & uncertainty

Full-stack development

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 research engineers who are excited to tackle unsolved problems. Progress is only as trustworthy as its measurement. As our model, data, and reasoning efforts multiply, every team needs to know, precisely and comparably, whether a change made the model better. Your mission is to build the central evaluation framework that the entire research organization runs on: the pipelines, the metrics, and the tools that turn results into shared understanding.

Responsibilities

• Design and build a central, reusable evaluation framework that every model and every team runs through

• Implement evaluation pipelines, benchmark suites, and baselines that make model quality measurable and comparable across efforts

• Build the visualization and dashboard tools that turn raw results into shared, actionable understanding for the whole team

• Establish sound statistical methodology for evaluation, so teams can distinguish real improvements from noise

• Partner with research and domain teams to translate what "good" means in each domain into standardized, automated metrics

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 software engineering skills and experience building data or evaluation pipelines at scale

• Experience turning research or model outputs into metrics, benchmarks, and visualizations that teams rely on

• Solid grasp of probability and statistics, with the judgment to design evaluations that measure what they claim to

• Full-stack range: comfortable building both backend pipelines and the frontend tools people read results in

• Owns deliverables end-to-end, from collecting requirements to autonomously driving execution