Staff Applied ML Engineer

Red Cell Federal is hiring a Staff Applied ML Engineer to build and deploy production AI systems for federal and national security missions.

You’ll work across LLMs, model fine-tuning and adaptation, agentic systems, RAG, evaluation, and inference, helping turn rapidly evolving AI capabilities into reliable software that operates in real mission environments.

This isn’t a pure research role, and it isn’t an ML infrastructure role removed from users. You’ll work alongside Forward Deployed and Software Engineers, occasionally directly with customers, to understand operational problems and determine how models, data, agents, and software can solve them.

Red Cell Federal’s platform is being designed to support interchangeable models, including specialized smaller models for edge use cases, and to operate across cloud, on-premise, and edge environments.

What You’ll Do

• Build production LLM-powered applications, AI agents, and agentic workflows
• Develop and own model evaluation, fine-tuning, adaptation, and experimentation pipelines
• Determine when to use prompting, RAG, fine-tuning, specialized models, or combinations of these approaches
• Build rigorous eval systems for model and agent quality, reliability, tool use, and task completion
• Develop retrieval and context systems across structured and unstructured mission data
• Optimize models and inference for production environments
• Deploy and improve models based on real-world performance and user feedback
• Partner with FDEs and customers to translate mission requirements into production AI capabilities
• Turn solutions developed for individual deployments into reusable platform capabilities

What We’re Looking For

• Significant experience building ML or AI systems and deploying in production
• Hands-on experience with modern LLMs and foundation models
• Deep expertise in several of the following:
• LLM fine-tuning / model adaptation
• Model evaluation
• RAG and retrieval systems
• Agentic systems and tool use
• Model serving / inference
• Embeddings and knowledge retrieval
• Synthetic data
• Guardrails and AI reliability
• Ability to move comfortably between ML experimentation and production engineering
• Ability to operate independently against ambiguous technical problems
• Interest in working close to users and seeing how AI performs against real operational workflows

Why This Role

The challenge here isn’t proving that an LLM can perform a task. It’s figuring out how to make AI reliable enough to use in production, measurable enough to know when it fails, adaptable enough to improve quickly, and practical enough to operate within real national security environments.

Red Cell Federal is focused on that “agentic last mile”: operationalizing and deploying AI against mission requirements rather than stopping at decision support or prototypes.

If you want to work deeply on the models and stay close enough to the problem to see whether what you built actually works, let’s talk!