RL Engineer

Wintermeyer Ventures; San Francisco (CA); Remote; USD 260,000 - 290,000; Full time

Job summary

Wintermeyer Ventures is seeking an experienced machine learning/software engineer to design RL environments, datasets, and evaluation methods for advanced AI models. You will contribute to RLHF/RLVR pipelines and work on real-world data, with opportunities to publish and experiment in a fast-moving team.

The role emphasizes backend or full-stack development, strong Python/TypeScript skills, and a CS degree or exceptional experience.

Qualifications

• BSCS or equivalent experience in computer science or related field.
• Experience building reinforcement learning environments or agent tasks.
• Strong backend or full-stack engineering skills in Python/TypeScript or similar languages.
• Experience measuring dataset quality and diversity, with AI research, publications, or side projects as signals.
• Ability to work effectively in a fast-moving, detail-focused team.

Responsibilities

• Design data slices and agent tasks that reveal model failure modes across domains.
• Build and refine evaluation rubrics and reward signals for RLHF and RLVR pipelines.
• Model annotator behavior and run experiments to improve model capabilities.
• Develop quantitative methods for measuring dataset quality, diversity, and impact on alignment.
• Create and manage real-world and synthetic data pipelines.
• Collaborate with research teams to translate training objectives into data and evaluation specifications.

Skills

Python

TypeScript

Backend development

Reinforcement learning

ML software engineering

Education

B.S. in Computer Science

Tools

PyTorch

TensorFlow

Git

Job description

About the Role

Join a small, early-stage team building training data and evaluation infrastructure for advanced AI models. You will design reinforcement learning environments, datasets, and evaluation methods that help models improve across complex, real-world tasks.

What You'll Do

• Design data slices and agent tasks that reveal meaningful model failure modes across areas such as finance, coding, and enterprise workflows.
• Build and refine evaluation rubrics and reward signals for RLHF and RLVR training pipelines.
• Model annotator behavior and run experiments to improve model capabilities.
• Develop quantitative methods for measuring dataset quality, diversity, and impact on model alignment and capabilities.
• Create and manage real-world and synthetic data pipelines.
• Work with research teams to translate training objectives into concrete data and evaluation specifications.

What We're Looking For

• One to eight years of experience as a machine learning or software engineer.
• Experience building reinforcement learning environments, or strong research experience developing agent tasks.
• Strong backend or full-stack engineering skills in Python, TypeScript, or similar languages.
• A computer science degree from a highly regarded institution, or an exceptional record of relevant experience.
• Experience measuring dataset quality and diversity, with AI research, publications, or relevant side projects as valuable signals.
• A bias for action, strong execution, and willingness to take on challenging, detailed work in a fast-moving team.

Compensation & Benefits

Salary range: $260,000 to $290,000 annually. Visa sponsorship is not available.

Location

Remote, with San Francisco, California as the primary location.