Staff Applied Machine Learning Engineer.

Staff Applied Machine Learning Engineer (Remote USA)

***This role requires expert level in development and deployment of machine learning models***

***US Citizenship or Greencard (Permanent US Residency) is required due to US Government contract requirements***

***You must be domiciled in the U.S. (must live permanently and maintain home address in the US)***

***As a step in the application process you will be required to provide legally authorized Identification to show verification of eligibility through third party provider CLEAR ID***

About us:

Let’s be real, AI isn’t magic; Legion was built to move beyond AI hype—delivering secure, reliable systems that work alongside the people tackling the world’s most critical challenges.

Born from a Department of Defense partnership and trusted by leaders across government and enterprise, Legion embeds intelligence inside complex systems, unlocking data, accelerating human workflows, and strengthening mission-critical systems. We don’t replace workflows—we optimize them, ensuring quality, efficiency, and reliability inside the platforms our partners already use.

With world-class collaborators like Palantir, Nvidia, HPE, and Oracle, we’re building intelligent infrastructure that enhances human capability and drives impact at the edge and across a range of enterprises.

We’re looking for bold thinkers and doers to join us in shaping the future of AI that’s secure, grounded, and built to work.

Job Summary:

We’re looking for a Staff Machine Learning Engineer to architect and build the intelligence layer behind our agentic AI platform, with a focus on search, navigation, and task-oriented user experiences. In this role, you’ll design and ship ML systems that enable intelligent retrieval, reasoning, planning, tool use, and adaptive navigation across the product. You’ll work across NLP, ML, knowledge representations, and production system design to create scalable, reliable, and high-impact AI experiences.

This role is ideal for someone who has deep experience building agentic systems, including reasoning loops, orchestration patterns, tool calling, memory/context management, and model fine-tuning, and who can apply that expertise to make search and navigation feel intelligent, proactive, and context-aware.

Responsibilities

• Lead the design and development of ML and NLP systems that power search, navigation, and agentic AI experiences.
• Innovate around our core iterative reasoning, retrieval, and search systems with agentic AI
• Develop, fine-tune, evaluate, and optimize machine learning and language models for production use.
• Design and build complex systems involving knowledge graphs, semantic representations, and advanced entity detection/extraction.
• Collaborate closely with product, platform, and engineering teams to own AI/ML solutions end-to-end, from design through deployment and iteration.
• Make sound technical decisions by understanding algorithmic complexity, model performance, latency, and production cost tradeoffs.
• Build, deliver, and maintain enterprise-grade products that meet high standards for quality, reliability, and speed.
• Support production systems, including debugging and resolving issues in distributed systems to ensure uptime and reliability.
• Clearly communicate technical decisions, tradeoffs, and limitations to both technical and non-technical stakeholders.
• Stay current with the latest advances in AI, NLP, and machine learning, and apply them thoughtfully to improve our products and platform.

Required Skills and Qualifications

• US Citizenship or Green Card required due to US Government contract requirements.
• Bachelor’s or Master’s degree in Computer Science, Machine Learning, NLP, or a related field; PhD preferred.
• 10+ years of experience in ML/MLOps engineering with a strong track record of building models and systems for large-scale production environments.
• Proven experience designing, building, evaluating, and optimizing agentic AI systems, including reasoning loops, tool use / tool calling, context management, multi-agent or workflow-based systems
• Strong systems thinking with an understanding of runtime complexity, infrastructure costs, and production scalability.
• Strong communication skills and the ability to collaborate effectively across cross-functional teams.
• Comfortable supporting production systems and debugging challenges in distributed environments.
• Demonstrated ability to use AI tools to accelerate research and development workflows.
• Growth mindset and low ego: eager to learn new tools and technologies, adapt quickly, and change course when needed.
• Strong commitment to collaboration, psychological safety, and technical excellence.

Preferred Qualifications

• Startup experience or comfort working in 0→1 product environments.
• Experience integrating ML models and data-driven algorithms into larger system architectures.