Data Scientist at Fuse

About Fuse We are building one of the most potentially consequential companies of the century. Our mission is to accelerate the world's transition to fusion energy while safeguarding humankind. This is not a normal company. This is not a normal job. We are committed for the long term to win. About the Role You will build our in-house simulation stack — the models that predict what our fusion machines will do, and the inference machinery that pulls physics out of shot data. This is not a “fit a curve to a dashboard” data science role. Core Responsibilities Build a coupled simulation framework for our DPF: pulsed-power circuit, sheath formation and run-down (snowplow / slug / Lee-type models, progressing toward MHD), pinch and instability development, and neutron and X-ray production. Implement radiation source-term models: thermonuclear vs. beam-target neutron-yield decomposition, bremsstrahlung and line-emission spectra, and anisotropy. Develop surrogate and reduced-order models so designers and physicists can iterate on parameter sweeps without standing up an HPC job each time. Build the data pipeline that ingests every shot's diagnostic stream, including Rogowski coils, B-dots, silver activation, time-of-flight neutron detectors, filtered diodes, and fast cameras, and joins it to predicted output for systematic residual analysis. Quantify uncertainty seriously: Bayesian inference over model parameters, identifiability analysis, and honest error bars on yield predictions. Stand up simulations of supporting machine outputs, including anode/cathode lifetimes, electrode erosion models, and capacitor-bank aging, so operations decisions are informed by physics rather than vibes. Publish internally with the same standards you'd publish externally: derivations written out, assumptions stated, code reviewed. Eventually grow a small simulation team. For now, you will be a force multiplier of one. Minimum Qualifications PhD in Physics, Applied Mathematics, Plasma Physics, Computational Science, Nuclear Engineering, or a closely related field. An exceptional Master's candidate with a strong publication record will be considered. Strong academic record, with a target GPA of 3.8+ from a competitive program. Demonstrable depth in at least one of: magnetohydrodynamics, kinetic plasma theory, radiation transport, or pulsed-power circuit modeling. Familiarity with the others. Fluency in vector and tensor calculus, Maxwell's equations in arbitrary geometries, hyperbolic PDEs and numerical schemes, Bayesian statistics, and optimization under constraints. Production-grade Python using NumPy, SciPy, xarray, JAX, or PyTorch for differentiable physics, with comfort dropping into C++ or Fortran when Python isn't fast enough. Experience writing simulation code from scratch, not just running someone else's solver. Ability to read a physics paper, identify the load-bearing assumption, and determine quickly whether it applies to our regime. Preferred Qualifications Hands-o