Machine Learning Engineer | Remote

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

Job summary

Crossing Hurdles is seeking a Member of Technical Staff, Forward Deployed (US Gov) for a hybrid role in Washington, DC. You will build and maintain data pipelines, design RL environments, and develop scalable model inference systems for high-stakes projects.

You will implement and maintain SDKs and integrations across frontier AI platforms, constructing agentic systems with modern LLM stacks while prioritizing security and reliability.

Qualifications

• Strong experience as a Python engineer with end-to-end project ownership.
• Experience with large language models or agentic systems (LangChain/LangGraph).
• Built or maintained data pipelines and ML infrastructure.
• Familiar with reinforcement learning workflows or simulation environments.
• Experience with distributed systems or scaling inference processes.
• Ability to operate 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 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.

Skills

Python

End-to-end ownership

Distributed systems

Reinforcement learning workflows

Security and reliability

Tools

LangChain

LangGraph

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.