About Code Metal

Code Metal is the leader in automated software engineering you can trust. As AI writes more of the world's code, the bottleneck in software has shifted from writing code to verifying it works, and AI cannot verify its own work with certainty. Code Metal takes a fundamentally different approach: constrain AI to what it does reliably, verify every step independently of the model using formal methods, and keep engineers in the loop on the decisions that matter. The result isn't code that probably works — it's code that is provably correct, with auditable proof. Customers including the U.S. Air Force, L3Harris, RTX, and Toshiba use Code Metal to modernize legacy code, optimize performance on real hardware, and move prototypes to production, fast. Founded in 2023 with offices in Boston and San Francisco, Code Metal is funded by Accel, Salesforce Ventures, B Capital, Smith Point Capital, J2 Ventures, Shield Capital, Overmatch, RTX, and others.

Learn more at codemetal.ai.

The Role

Code Metal's engineering teams are building AI-driven code transpilation, modernization, optimization, modeling and simulation solutions. They all need the same foundations: models to serve, agents to run, context to manage, and results to measure. Our AI Platform team builds those foundations.

As a Senior Software Engineer on the AI Platform team, you'll design, build, and operate parts of the AI enablement stack our engineers depend on: GPU inference serving, model gateways, agent harnesses, context engineering, observability, and AI experimentation management. It is an internal platform, but we're building it to product standard.

Over time you will own 1 area, such as model serving, agentic infrastructure, or experimentation and telemetry, while you also contribute across the rest of the stack. We don't expect you to arrive with every skill on this page.

This is an engineering role first. Most of your time goes to designing, building, and operating production systems. You'll also need solid data science and AI research fundamentals: you'll work closely with our Applied AI Research team, and you'll sometimes run experiments when a platform decision needs evidence.

Focus Areas

Each engineer on the team brings depth in at least 1 of these areas:

Model serving: Deploy, benchmark, and tune elastic, production inference for open-weight models on runtimes such as vLLM, SGLang, and TensorRT-LLM. Run the model gateways that teams use to reach self-hosted and commercial models.

Agentic infrastructure: Build agent harnesses, orchestration primitives, and context-engineering services (memory, retrieval, and data discovery) that product teams compose into reliable, verifiable workflows.

Observability and experimentation: Instrument the stack end to end, for example, with OpenTelemetry traces and service metrics. Build evaluation harnesses and the experiment-tracking and artifact layer that lets engineers and researchers reproduce and compare results.

Core Responsibilities

Own 1 or more platform components end to end, from design doc to production operation.

Build and operate the services in your focus area, and contribute across the rest of the stack.

Write clean, well-tested code, and hold AI-generated code to the same bar: correct, modular, and fully tested.

Debug hard production issues in the components you own, such as tail latency, GPU memory pressure, or agent runs that fail or loop.

Scope and estimate efforts with internal customers, and raise risks early.

Review teammates' code and designs, and help onboard new engineers.

Run focused benchmarks and experiments when a platform decision needs evidence.

Required Qualifications

Production-grade Python and solid platform engineering fundamentals: API and service design, distributed systems, containers and orchestration (such as Kubernetes), CI/CD, and testing.

Production experience in at least 1 focus area:

Model serving: deploying and tuning LLM inference, or running an LLM gateway or API gateway.

Agentic infrastructure: shipping agentic systems, or building context-engineering services such as retrieval-augmented generation, embeddings, and vector or hybrid search.