> Markdown version of [/jobs/ext/1742885-2026-phd-residency-physical-ml-hardware-in-the-loop-future-of-compute](https://www.wearedevelopers.com/jobs/ext/1742885-2026-phd-residency-physical-ml-hardware-in-the-loop-future-of-compute). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # 2026 PhD Residency - Physical ML & Hardware-in-the-Loop (Future of Compute) - **Company:** Google LLC - **Location:** Mountain View, CA, United States - **Salary:** $109,000.0 - $157,000.0 - **Contract:** Internship / Graduate position - **Skills:** Artificial Neural Networks, C++ (Programming Language), Custom Software, Software Debugging, Dynamical Systems, Hardware-In-The-Loop Simulation, Python (Programming Language), Machine Learning, Non-Volatile Memory, Tensorflow, Pytorch, Information Technology, SQL Server Management Studio (SSMS) - **Published:** July 3, 2026 - **Apply:** https://dejobs.org/x/x/D752029539D644758A5CC0A1DC40AE7B/job/ ## About the Role * Currently enrolled in a PhD program in Computer Science, Electrical Engineering, Applied Physics, or a related STEM field. * Strong 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. It'd be great if you also had these: * Familiarity with non-volatile memory architectures (RRAM/memristors) or active analog coupled-oscillator circuits. * Experience in high-velocity characterization of integrated circuits or neuromorphic accelerators. ## Description This is the flagship moonshot for 'The Future of Compute' at X (the Moonshot Factory). This project is aimed at moving away from simulating physics on digital chips and instead building physical machines whose dynamics ARE the computation itself, achieving a 1,000,000x improvement in useful compute per Joule. This residency focuses on bridging the gap between physical dynamical systems and machine learning code. Instead of modeling physics in a vacuum, you will work directly with physical silicon test chips and laboratory instrumentation to make them trainable. You will build the software-to-hardware calibration pipelines that demonstrate we can train a real physical substrate to match digital baseline accuracy at a fraction of the energy cost. How you will make 10x impact: * Collaborate on building robust, real-time "hardware-in-the-loop" (HIL) training pipelines that connect active silicon oscillator arrays and memristor test-boards directly to PyTorch/JAX ML frameworks. * Independently design and execute experimental protocols to train real physical hardware on machine learning benchmarks (e.g., MNIST, CIFAR) using physics-aware backpropagation and equilibrium propagation. * Perform automated, high-velocity physical measurements to characterize non-linear physical dynamics, mapping real hardware output against digital twin simulators to quantify "reality-gap" discrepancies. * Analyze the effect of physical device noise, ambient thermal fluctuations, and RRAM resistance drift on live training convergence, and implement software-level algorithmic mitigation strategies (e.g., noise-injection training). * Investigate the physical execution of sparse, post-transformer architectures (Mamba-class SSMs) directly on physical neuromorphic arrays by developing physical weight-mapping and routing schemes. * Develop clean, reusable software interfaces (using Python, C++, and PyVISA) to automate lab instrumentation, making real-world physical training runs as seamless and reproducible as digital software training., * To be embedded in an agile, confidential project team focused on "Monkey First" thinking-identifying and tackling critical physical risks through immediate, hands-on laboratory iteration. * Direct mentorship from experts in neuromorphic systems, mixed-signal test engineering, and physics-aware machine learning. * A collaborative environment that values technical rigor, physical measurements, and the willingness to iterate rapidly through "V0.crap" hardware setups. ## Related Videos - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [Strange New Worlds: shaping the future of the digital age](https://www.wearedevelopers.com/videos/677-strange-new-worlds-shaping-the-future-of-the-digital-age) - [Product discovery techniques developers should know](https://www.wearedevelopers.com/videos/1009-product-discovery-techniques-developers-should-know) - [Machine learning in the browser with TensorFlowjs](https://www.wearedevelopers.com/videos/155-machine-learning-in-the-browser-with-tensorflowjs) - [Schroedinger's cat: Thinking in- and outside the box of quantum mechanics](https://www.wearedevelopers.com/videos/216-schroedinger-s-cat-thinking-in-and-outside-the-box-of-quantum-mechanics) - [Geometric deep learning for drug discovery](https://www.wearedevelopers.com/videos/264-geometric-deep-learning-for-drug-discovery) ## Related Articles - [Top 6 Hackathons for Developers in 2023](https://www.wearedevelopers.com/magazine/263-top-6-hackathons-for-developers-in-2023) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Got AI ideas but no money? Here are 10 free ways to level up your AI skills with Google Cloud](https://www.wearedevelopers.com/magazine/600-got-ai-ideas-but-no-money-here-are-10-free-ways-to-level-up-your-ai-skills-with-google-cloud) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Dev Digest 121 - AI goes offline](https://www.wearedevelopers.com/magazine/456-dev-digest-121-ai-goes-offline)