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2026 PhD Residency - Physical ML & Hardware-in-the-Loop (Future of Compute)

Google X· X (Moonshot Factory)
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Mountain View, CA$109k - $157kinternshipPosted 3mo ago

About the role

PhD residency focusing on bridging physical dynamical systems with machine learning by training on physical silicon test chips and lab instrumentation. Develop ML pipelines that couple hardware-in-the-loop with PyTorch/JAX, and explore physics-aware training on neuromorphic hardware.

Responsibilities

  • Collaborate on building real-time hardware-in-the-loop training pipelines connecting silicon oscillator arrays and memristor test-boards to PyTorch/JAX frameworks.
  • Independently design and execute experimental protocols to train real physical hardware on ML benchmarks (e.g., MNIST, CIFAR) using physics-aware backpropagation and equilibrium propagation.
  • Perform automated, high-velocity physical measurements to characterize non-linear dynamics and map hardware output to digital twin simulators to quantify reality-gap discrepancies.
  • Analyze effects of physical device noise, ambient thermal fluctuations, and RRAM resistance drift on live training convergence; implement software-level mitigation strategies (e.g., noise-injection training).
  • Investigate physical execution of sparse, post-transformer architectures (Mamba-class SSMs) on physical neuromorphic arrays via weight-mapping and routing schemes.
  • Develop clean, reusable software interfaces (Python, C++, PyVISA) to automate lab instrumentation for seamless, reproducible physical training runs.

Qualifications

  • Currently enrolled in a PhD program in Computer Science, Electrical Engineering, Applied Physics, or a related STEM field.
  • Hands-on experience in hardware-in-the-loop (HIL) systems, laboratory automation, or writing custom software interfaces (e.g., Python, C++) to control physical instruments (oscilloscopes, parameter analyzers, source-measure units).
  • Proficiency in writing non-standard neural network training loops and custom physical simulator components in PyTorch, JAX, or C++.
  • Solid understanding of physical non-idealities (noise, drift, device-to-device variability) and how to represent them mathematically in training frameworks.
  • Ability to work in a physical laboratory environment, debug electrical setups, and rapidly prototype hardware-software interfaces.

Skills

Required:Hardware-in-the-loop (HIL) systemsPythonPyTorch/JAXLab instrumentation control (oscilloscopes, SAs, SMUs)Noise and drift modeling in training
Nice to have:C++

Benefits

  • Base US salary range $109,000 - $157,000 + benefits
  • Equal opportunity employer policies and accommodations via x-accommodation-request@x.team
Last updated Oct 22, 2026. Interested? Apply on the company site.
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Welcome to X, Alphabet's Moonshot Factory. We create radical technologies to help solve some of the world’s hardest problems.

Welcome to X, Alphabet's Moonshot Factory. We create radical technologies to help solve some of the world’s hardest problems.

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