Satnam Singh is a software engineer with over a decade of experience applying functional programming, formal methods and machine learning to hardware and systems design. He has moved between industry and academia—shaping FPGA and silicon designs at Xilinx, Microsoft Research and Groq, building ML-focused compilers and verified roots of trust at Google, and now combining ML with the Lean theorem prover for hardware verification at Harmonic. His work blends practical backend contributions to high-profile open source projects like the Kubernetes API with deep research in theorem proving (Coq, Agda), Lava/Bluespec hardware DSLs and Haskell. An elected member of IFIP working groups and a Fellow of the IET, he uniquely bridges theorem provers and chip RTL, often using LLMs to assist formal proofs. Based in Los Altos, he thrives on turning formal specifications into verifiable silicon and tools that make hardware safer and faster.
11 years of coding experience
24 years of employment as a software developer
PhD Computing Science, PhD Computing Science at University of Glasgow
Analyzes resource usage and performance characteristics of running containers.
Role in this project:
Back-end Developer
Contributions:26 commits in 4 days
Contributions summary:Satnam primarily contributed to the project by making minor Go-style suggestions and fixing typos within the codebase. They modified the `api/handler.go`, `client/client.go`, `client/client_test.go`, `manager/manager.go`, `container/raw/handler.go`, `container/container.go` and other associated test files. Their changes focused on code style and comment corrections.
The canonical location of the Kubernetes API definition.
Role in this project:
Back-end Developer
Contributions:17 commits in 11 months
Contributions summary:Satnam contributed to the Kubernetes API definition by implementing validation checks for service port numbers and resource versions. They made minor code adjustments to align with code review feedback, including package name modifications and camelCase usage. Furthermore, they addressed error handling, specifically by switching to the "Too Many Requests" response code and refining error messages for update operations. The user's commits demonstrate a focus on improving API validation, error handling, and code quality within the Kubernetes API.
apikubernetes-apilocationkubernetesdefinition
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