Machine Learning Engineer

• About Adelphi
• Adelphi's Vision is to build the biggest AI Deployment company for National Security, which requires talent that is extremely good at problem solving, gets excited about National Security challenges, and thrives in software factories. We're executing a Mission that (1) Identifies the core bottlenecks in Warfighting, Intelligence and the Enterprise, (2) Assigns top talent with backgrounds in systems and machine learning engineering, and (3) Finds repeatability in solution development to target similar offices across the DoW/IC. The business has 10x'd in under two years and has built trust with the senior most leaders at the Department of War and Intelligence Community.

About the Role: The Machine Forward Deployed Learning Engineer position requires a mix of software development, LLM Ops, and SecDevOps practices, resulting in an exciting, fast-paced engineering role. This role requires the ability to contribute to solutions across the full LLM stack, from the OS, storage, and network up to the API and transport layer. Experience in the defense or intelligence fields is required. You will contribute to end-to-end delivery of agentic, full-stack systems built on top of frontier models, from first prototype to stable production, embedded alongside our defense and intelligence customers.

Location: This role is based in the DC/Metro area - we follow a hybrid work model with three days per week in office or onsite with customers.

Clearance Requirement: An active U.S. Government clearance is required.

Is this you?

We are an AI-native engineering team. We expect every engineer to leverage LLMs and AI tooling as a core part of how they design, build, ship, and operate agentic systems that turn frontier-model capability into mission outcomes.

You use AI coding tools (Claude Code, Cursor, Copilot) daily and instinctively.

You leverage LLMs across the development lifecycle, and stay current with emerging models and tooling.

You have working knowledge of modern agent frameworks and SDKs (LangGraph, OpenAI Agents SDK, Claude Agent SDK, AutoGen, or similar).

Familiarity with MCP or similar LLM integration frameworks.

You have a clear-eyed view of AI limitations. You know when to trust AI-generated output and when to verify.

Expectations:

Contribute to end-to-end delivery of agentic, full-stack systems from prototype to production, embedded alongside defense and intelligence customers.

Build and deploy ML services leveraging LLMs, embeddings, RAG, and agent orchestration into production environments, including classified and air-gapped ones.

Work directly with customers to understand problems, support delivery sequencing, and ship AI applications under real-world constraints.

Help codify repeatable patterns into reusable tools and building blocks that help the team ship faster.

Bonus Points:

Familiarity with infrastructure management (Docker, Kubernetes, AWS).

Exposure to encryption, authentication, Linux systems administration, DevOps, or SRE.

Any production experience with agentic services or forward-deployed AI applications.

Experience in a customer-facing or embedded delivery role.

Exposure to federated or privacy-preserving data architectures.