AI Engineer, RL & Evaluations for Creative Domains

Raydar; San Francisco (CA); Vor Ort; USD 175.000 - 275.000; Vollzeit

Zusammenfassung

Raydar is a seed-stage AI company in San Francisco building the data and infrastructure layer to improve model quality in creative domains. You will join as AI Engineer, RL & Evals to design, implement, and scale RL environments, evaluation systems, and production-grade backend pipelines.

The role blends back-end and ML work, requiring shipping production code end to end and collaborating with research teams to push model quality in design-oriented tasks.

Qualifikationen

• 2-7 years of relevant AI or reinforcement learning engineering experience.
• Strong full-stack or backend engineering skills and demonstrated production shipping experience.
• Experience with Python, PyTorch, RL, ML evaluation frameworks and LLMs.
• Knowledge of distributed systems and data pipelines; relevant scraping, indexing or agent-harness work is valuable.
• A computer science degree and fluent English communication.

Aufgaben

• Build and scale RL environments and evaluation frameworks for creative, non-verifiable domains.
• Design tasks and grading rubrics that capture design quality and taste.
• Build agent harnesses, context layers and supporting product infrastructure.
• Collaborate with internal research teams and frontier labs to improve model output quality.
• Ship production code end to end and build reliable backend systems and data pipelines.

Kenntnisse

Python

PyTorch

Reinforcement Learning

ML Evaluation

LLMs

Distributed Systems

Data Pipelines

Full-Stack

Backend Engineering

Production Shipping

English Communication

Ausbildung

CS Degree

Tools

Scraping

Indexing

Agent Harness

Jobbeschreibung

Raydar is a seed-stage AI company in San Francisco building the data and infrastructure layer to improve model quality in creative domains. You will join as AI Engineer, RL & Evals to design, implement, and scale RL environments, evaluation systems, and production-grade backend pipelines.

The role blends back-end and ML work, requiring shipping production code end to end and collaborating with research teams to push model quality in design-oriented tasks.