Staff Scientist, Action-Conditioned Compute World Models

General Diffusion, Inc.; San Francisco, Northern (CA, KY); Hybrid; USD 180,000 - 240,000; Full time

Job summary

General Diffusion, Inc. seeks a Member of Technical Staff to model world dynamics and forecast workload behavior before real-machine placement decisions. You will own predictors and evidence, collaborating with RL, runtime, and data teams on validation and safe usage of forecasts.

The role emphasizes action-conditioned state prediction, distribution-shift evaluation, and rigorous experimental practice across hardware configurations and workloads.

Qualifications

• Experience with world models, system identification, probabilistic forecasting, learned dynamics or model-based RL including action-conditioned state prediction.
• Experience evaluating models under distribution shift with held-out environments, hardware configurations, workload regimes or time horizons.
• Experience instrumenting or analyzing real compute systems through traces, telemetry, profiler data or performance counters; intuition for latency, throughput and contention.
• Strong experimental engineering skills to turn messy measured behavior into reproducible datasets, training runs, ablations and failure analyses.

Responsibilities

• Develop action-conditioned dynamics models that forecast compute-system behavior across hardware and workload timescales.
• Define target variables and uncertainty estimates for latency, throughput, contention, headroom, with diagnostics for miscalibration and failure modes.
• Design evaluation splits; measure transfer, calibration and degradation beyond aggregate accuracy.
• Use replay and shadow-mode experiments to test predicted consequences before production decisions.
• Work with Measurement & Data Infrastructure to specify trace fields, provenance, and reproducible training/evaluation slices.
• Publish a predictor contract outlining forecasts, uncertainty, assumptions and limits for RL compute environments and placement ensembles.

Skills

World models

System identification

Probabilistic forecasting

Learned dynamics

Model-based reinforcement learning

Action-conditioned prediction

Tools

Execution traces

Telemetry

Profiler data

Performance counters

Job description

General Diffusion, Inc. seeks a Member of Technical Staff to model world dynamics and forecast workload behavior before real-machine placement decisions. You will own predictors and evidence, collaborating with RL, runtime, and data teams on validation and safe usage of forecasts.

The role emphasizes action-conditioned state prediction, distribution-shift evaluation, and rigorous experimental practice across hardware configurations and workloads.