Python AI Engineer - RL/LLM Systems for Secure Gov Tech

Crossing Hurdles; United States; On-site; USD 350,000 - 500,000; Full time

Job summary

Crossing Hurdles in Washington DC is seeking a Member of Technical Staff, Forward Deployed for US Gov initiatives. You will build data pipelines for model training and research, design RL environments, and scale inference systems in high-security settings, coordinating with partners on sensitive, high-impact use cases.

The role requires strong Python expertise with end-to-end ownership, experience with large language models or agentic systems (LangChain, LangGraph), and proven ML infrastructure

Qualifications

• Experience as a Python engineer with end-to-end ownership of projects.
• Experience with large language models or agentic systems (LangChain, LangGraph).
• Built or maintained data pipelines and ML infrastructure.
• Familiarity with reinforcement learning workflows or simulations.
• Experience with distributed systems or scaling inference processes.
• Comfort operating in high-security, high-reliability environments.

Responsibilities

• Build and maintain data pipelines for model training, evaluation, and research workflows.
• Design and run reinforcement learning environments for experimentation and training.
• Develop and scale model inference systems to enhance performance and reliability.
• Implement and maintain SDKs and integrations across frontier AI platforms.
• Construct agentic systems using modern large language model stacks, ensuring security and reliability.
• Collaborate directly with partners in forward-deployed settings on sensitive, high-impact use cases.

Skills

Python

End-to-end ownership

Reinforcement learning

Large language models

Distributed systems

Security-focused

Tools

LangChain

LangGraph

Pipelines

Job description

Position: Member of Technical Staff, Forward Deployed (US Gov)

Type: Full-time

Compensation: $350K - $500K/yr

Location: Hybrid - Washington, D.C.

Role Responsibilities

• Build and maintain data pipelines for model training, evaluation, and research workflows.
• Design and run reinforcement learning environments for experimentation and training.
• Develop and scale model inference systems to enhance performance and reliability.
• Implement and maintain software development kits and integrations across frontier AI platforms.
• Construct agentic systems using modern large language model stacks, ensuring security and reliability.
• Collaborate directly with partners in forward-deployed settings on sensitive, high-impact use cases.

Requirements:

• Have strong relevant experience as a Python engineer with end-to-end ownership of projects.
• Have experience with large language models or agentic systems, such as LangChain or LangGraph.
• Have built or maintained data pipelines and machine learning infrastructure.
• Be familiar with reinforcement learning workflows or simulation environments.
• Have experience with distributed systems or scaling inference processes.
• Be comfortable operating in high-security and high-reliability environments.

Application Process:

• Easy Apply on LinkedIn
• Participate in resume evaluation & interview stage