AI-driven, formally verified code translation from algorithms to hardware
Code Metal builds AI tooling that translates and verifies code for deployment onto embedded and edge hardware, with a stack spanning Coq, Polyspace and Clang alongside Verilog, VHDL, Vitis HLS and CUDA. That mix of formal-verification tools with FPGA/ASIC design languages signals a product bridging DSP algorithms and silicon. Hiring is accelerating and engineering-heavy and senior-skewed, with legal and a Head of Security role pointing toward regulated government work.
Notable leadership hires: UX Lead, Director of Growth, Head of Security
Code Metal is a Boston-based, privately held software company founded in 2023, with 51–200 employees. It develops AI and large language model tooling for code generation and translation aimed at mission-critical industries. Its focus areas include embedded systems, edge processing, compilers and AI agents, with formal methods used to make outputs verifiable. The company targets deployments of complex algorithms onto constrained hardware such as FPGAs and ASICs, including government customers. It hires in the United States and the United Kingdom.
Code Metal uses Python, C++, Rust, Coq, Polyspace, Clang, Verilog, VHDL, Vitis HLS, CUDA and MATLAB, running on AWS, GCP and Azure with Kubernetes, Terraform, Helm and ArgoCD.
Yes. Hiring is accelerating and weighted heavily toward senior and principal engineering roles, with additional openings in sales, product, legal and design across the United States and United Kingdom.
Current work includes a core C++ simulation runtime, HLS integration and RTL code generation, agentic AI pipelines, integrating formal methods into its products, and customer proof-of-concept development.
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Code Metal's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.