Research Engineer | Remote

Crossing Hurdles; United States; Remote; USD 400,000 - 800,000; Full time

Job summary

Crossing Hurdles is seeking a remote Senior Member of Technical Staff to lead research engineering in reinforcement learning. The role focuses on building self-contained RL environments, scalable pipelines, and robust evaluation systems to accelerate AI experimentation.

Ideal candidates will have deep RL expertise, a track record of scaling experimentation infrastructure, and strong communication skills to document progress and findings for a distributed team.

Qualifications

• Deep experience in Reinforcement Learning, including environment design and training dynamics.
• Proven ability to build and scale RL systems, pipelines, or experimentation frameworks.
• Expertise in automation and data generation, including synthetic data pipelines.
• Familiar with automated evaluation systems, model validation, and quality assurance workflows.
• Strong technical writing and communication skills.

Responsibilities

• Architect self-contained RL environments that capture complex, real-world tasks, including reward functions, verifiers, and evaluation logic.
• Design and scale episode pipelines and multi-component training processes to support reproducible experimentation.
• Build automated data generation systems, leveraging synthetic data to accelerate training cycles without compromising quality.
• Develop and integrate AI-driven evaluation and quality assurance systems for automated grading, validation, and feedback loops.
• Fine-tune and optimize open-source RL models using internally generated datasets and custom training strategies.
• Establish benchmarking frameworks to measure model capability, robustness, and data quality across tasks.

Skills

Reinforcement Learning

Environment design

Experimentation frameworks

Automation & data generation

Open-source ML models

Tools

RL frameworks

Automation tooling

Job description

Position: Member of Technical Staff, Research Engineering

Type: Full-time

Compensation: $400K - $800K/yr

Location: Remote

Commitment: 10-40 hrs/week

Role Responsibilities

• Architect self-contained RL environments that capture complex, real-world tasks, including reward functions, verifiers, and evaluation logic.
• Design and scale episode pipelines and multi-component training processes to support reproducible experimentation.
• Build automated data generation systems, leveraging synthetic data to accelerate training cycles without compromising quality.
• Develop and integrate AI-driven evaluation and quality assurance systems for automated grading, validation, and feedback loops.
• Fine-tune and optimize open-source RL models using internally generated datasets and custom training strategies.
• Establish benchmarking frameworks to measure model capability, robustness, and data quality across tasks.

Requirements

• Deep experience in Reinforcement Learning, including environment design and training dynamics.
• Strong track record of building and scaling RL systems, pipelines, or experimentation frameworks.
• Proficient in automation and data generation, including synthetic data pipelines.
• Familiar with automated evaluation systems, model validation, and quality assurance workflows.
• Experienced in fine-tuning and evaluating open-source ML models.
• Clear, concise communicator with strong technical writing skills.

Application Process

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