Member of Technical Staff, Research Engineering

Zekai; United States; Remote; USD 200,000 - 300,000; Full time

Benefits offered by this job

Equity compensation

Performance-based bonuses

Health insurance reimbursement

401(K) plan with company match

Remote-first workforce

Job summary

Zekai is seeking a Research Engineer to design and scale reinforcement learning environments, training pipelines, and evaluation systems for cutting-edge AI models. This role blends research with production, translating ideas into high-performance software.

You will build automated data generation, perform fine-tuning on open-source models, and contribute to benchmarking and partner evaluations while collaborating in a fast-paced, remote-first team.

Qualifications

• 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.
• Comfortable operating in fast-paced, research-driven, and highly collaborative environments.

Responsibilities

• Architect self-contained RL environments with reward functions, verifiers, and evaluation logic.
• Design and scale episode pipelines and MCPs for reproducible experiments.
• Build automated data generation systems using synthetic data to accelerate training cycles.
• Develop AI-driven evaluation and QA systems for automated grading, validation, and feedback.

Skills

Reinforcement Learning

Environment design

RL systems scaling

Pipelines

Automation

Data generation

Synthetic data

Evaluation systems

Model validation

Quality assurance

Fine-tuning models

Open-source ML

Technical writing

Communication skills

Collaborative work

Tools

None

Job description

Job Title: Member of Technical Staff, Research Engineering

Job Type: Full-time

Location: Remote

The Role

We are seeking a Research Engineer to operate at the frontier of Reinforcement Learning (RL), developing novel environments, training pipelines, and evaluation systems that advance the capabilities of modern AI models. This role sits at the intersection of research and production, translating experimental ideas into scalable, high-performance systems.

What You'll Work On

• 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 (MCPs) 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.
• Contribute to the release and analysis of evaluations on internal and external benchmark platforms (e.g., our partner benchmarks).

What We're Looking For

• 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.
• Comfortable operating in fast-paced, research-driven, and highly collaborative environments.

Preferred

• Experience publishing benchmarks, evaluations, or research artifacts.
• Familiarity with evaluation ecosystems (e.g., our partner benchmarks or similar frameworks).
• Background in scalable infrastructure for large-scale RL experimentation.

Compensation & Benefits Notice

The national pay range for this full-time position is base salary of $200,000 –$300,000 USD. All employees are eligible for equity compensation, and employees may also receive performance-based bonuses, dependent on role and subject to company policies. our partner provides a comprehensive benefits package, including up to 100% reimbursement for health-insurance premiums, paid time off, a 401(K) plan with a company match, and additional benefits designed to support a high-performing, remote-first workforce.

our partner is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex (including pregnancy, sexual orientation, or gender identity), national origin, age, disability, genetic information, veteran status, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance and/or a reasonable accommodation during the application process, reach out to support@our partner.

Our hiring process utilizes artificial intelligence tools to assist in candidate screening and assessment. Our AI tools are designed to complement, not replace, human decision-making.

Disclaimer

The information contained in this job posting, including but not limited to role responsibilities, qualifications, compensation, and benefits, is provided for informational purposes only and does not constitute a binding offer of employment. our partner reserves the right to amend, modify, or withdraw any portion of this posting at its sole discretion and without prior notice. All employment decisions are made in accordance with applicable laws and regulations.

This is a 100% remote contract role, open to applicants worldwide in the countries the employer accepts.