Machine Learning Engineer

About Adelphi: Adelphi builds AI/ML-enabled secure data access and sharing technology for the U.S. intelligence and defense communities, using its Connector software product to enable federated data discovery that cuts intelligence-sharing time from months to minutes. The company's mission is to eliminate data silos, build trust in automation without compromising security, and improve information flow across mission-critical environments. Adelphi closed a $7M Seed round in August 2025 and has Customers across the Intelligence Community and the Department of War. 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; remote candidates will be considered with 25% travel expected. Clearance Requirement: An active U.S. Government clearance is strongly preferred, but we are open to clearance eligible candidates. 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. Benefits: Healthcare coverage: 100% employee premium and 50% dependents premium coverage of a platinum-level plan. 401K with 2% company match. $500 monthly Physical and Mental Health reimbursement program. Unlimited time-off policy. Competitive salary and equity compensation. Opportunity to work on impactful projects in the national security sector. Career growth and leadership opportunities in a dynamic, innovative environment.