Hybrid | Forward Deployed Engineer, U.S. Government — $300,000–$500,000/year

24-Mag•New York, NY

3d•Hybrid

About The Position

This is a full-time, hands-on individual contributor role focused on developing and deploying advanced AI solutions in U.S. Government and other mission-critical technology environments. The engineer will work across applied AI, software engineering, infrastructure, and production deployment, collaborating directly with government partners to translate operational requirements into reliable technical systems. The work includes LLM applications, agentic AI architectures, machine learning infrastructure, data pipelines, distributed systems, and forward-deployed solution development in secure and high-assurance settings. The position spans the engineering lifecycle, from discovery and architecture through implementation, deployment, and ongoing iteration. The engineer will build AI systems and supporting infrastructure, integrate models and data into operational workflows, evaluate system performance and reliability, and provide hands-on technical support to partners. The role is hybrid in the Washington, DC area, and relocation to that area is required. Compensation is $300,000–$500,000 per year.

Requirements

Strong professional software engineering background with demonstrated expertise in Python.
Proven experience independently designing, developing, and deploying complex technical systems.
Hands-on experience building applications with large language models and related AI technologies.
Practical expertise developing agentic AI systems, autonomous workflows, or tool-using AI applications.
Familiarity with multi-agent architectures and retrieval-augmented generation (RAG).
Experience building or maintaining data pipelines, machine learning infrastructure, or AI evaluation systems.
Strong understanding of distributed systems, software architecture, and production engineering.
Ability to own technical projects from initial requirements through deployment and iteration.
Experience working directly with external partners, clients, or operational stakeholders on technical implementations.
Ability to translate complex operational requirements into practical engineering solutions.
Experience developing reliable AI applications in production environments.
Understanding of software security, system reliability, and engineering considerations in high-assurance environments.
Ability to work independently in technically complex, evolving project environments.
Strong technical problem-solving, debugging, and analytical reasoning capabilities.
Excellent written and verbal communication skills, including collaboration with technical and non-technical stakeholders.
Adaptability and ability to manage engineering priorities in fast-moving environments.
Willingness to relocate to the Washington, DC area.
Substantial technical ownership across the engineering lifecycle, with strong independent execution, cross-functional collaboration, and production engineering capabilities.

Nice To Haves

Familiarity with reinforcement learning workflows, simulation environments, or model experimentation.
Experience supporting U.S. Government, defense, intelligence, or national security missions.
Active U.S. security clearance, or eligibility and willingness to obtain one.
Experience with defense technology organizations, government-focused technology companies, or early-stage startups.
Familiarity with mission-critical government technology deployments and relevant operational requirements.
Experience working in secure or high-reliability technical environments.

Responsibilities

Design, develop, and deploy agentic AI systems for complex operational and government mission requirements.
Build LLM-powered applications using multi-agent architectures, tool-using agents, and autonomous workflows.
Develop retrieval-augmented generation (RAG) systems and integrate relevant knowledge retrieval capabilities.
Implement human-in-the-loop mechanisms and controlled autonomous decision-making workflows.
Evaluate and improve AI application reliability, performance, and operational effectiveness.
Work directly with government partners to understand operational objectives and technical requirements.
Translate mission-specific challenges into practical AI engineering solutions.
Support forward-deployed engineering from initial discovery through implementation and production adoption.
Integrate AI capabilities into existing workflows and technical environments in collaboration with operational stakeholders.
Provide hands-on engineering support to improve system functionality, usability, and mission alignment.
Build and maintain scalable data curation pipelines for AI model development and evaluation.
Develop infrastructure for model experimentation, inference, testing, and deployment.
Integrate data processing systems, model services, and distributed infrastructure.
Support AI training and evaluation workflows through engineering and infrastructure development.
Improve the performance, scalability, and maintainability of production AI systems.
Contribute to applied AI experimentation and transition research capabilities into production systems.
Develop workflows for model evaluation, reinforcement learning, and simulation-based experimentation.
Integrate models, data sources, external tools, and operational services into cohesive AI applications.
Evaluate emerging AI capabilities and identify practical deployment opportunities.
Assess system behavior, reliability, and technical effectiveness using rigorous engineering methods.
Own technical solutions through discovery, architecture, implementation, deployment, and ongoing iteration.
Design software architectures for secure, high-reliability, mission-critical environments.
Apply engineering practices for testing, debugging, monitoring, and operational reliability.
Address integration challenges involving distributed services, AI infrastructure, and production workloads.
Collaborate with engineering and operational stakeholders to maintain system quality, security, and performance.