ML Evaluation Architect for AI Benchmarks

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

Job summary

Causal is building a Large Physics foundation Model to predict and influence physical systems, starting with weather. We seek research engineers to design a central evaluation framework and reusable pipelines that span models and teams.

You will implement evaluation pipelines, benchmark suites, baselines, and visualization tools to turn results into shared, actionable understanding. Strong SWE and statistics are essential.

Qualifications

• 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.

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

Skills

Software engineering

Data pipelines

Metrics & benchmarks

Statistics

Full-stack development

Job description

Causal is building a Large Physics foundation Model to predict and influence physical systems, starting with weather. We seek research engineers to design a central evaluation framework and reusable pipelines that span models and teams.

You will implement evaluation pipelines, benchmark suites, baselines, and visualization tools to turn results into shared, actionable understanding. Strong SWE and statistics are essential.