Lead Research Engineer, Data Quality

Wwshemi; United States; Presencial; USD 130.000 - 225.000; Jornada completa

Descripción de la vacante

Wwshemi seeks a leader to head the data quality team at an AI/ML startup focused on reinforcement learning and agent evaluation. You will shape strategy and build systems to measure and improve training data for real-world tasks.

You will lead the development of scalable validation methods and dashboards, collaborate with researchers and data vendors, and mentor engineers to ensure rigorous, production-ready data workflows in a fast-moving startup environment.

Formación

• At least 5 years of research or engineering experience, including building AI or machine learning data evaluation systems.
• Experience leading technical projects or teams through ambiguous problems, from definition to implementation and iteration.
• Advanced proficiency with Python, Docker, and Linux.
• Background in AI research, evaluation, post-training, synthetic data, benchmarks, or data quality infrastructure.
• Strong understanding of what makes agent training data realistic, learnable, diverse, reliable, and useful.
• Experience designing metrics, experiments, and quality assurance processes, and translating research into production systems.
• Experience working with subject-matter experts and converting domain judgment into scalable review or data generation workflows.
• Clear written communication, strong attention to detail, and ability to work independently in an early-stage startup environment.

Responsabilidades

• Lead development of systems that evaluate tasks across reinforcement learning environments, synthetic data, benchmarks, and specialized workflows.
• Define data quality strategy, establish standards, and design experiments to assess agent outputs.
• Develop scalable methods to validate synthetic data, including failure analysis, task mutation checks, and trajectory audits.
• Work with research engineers, domain experts, and data vendors to diagnose quality issues and improve data generation workflows.
• Turn research insights into production tools, dashboards, validation pipelines, and feedback loops.
• Develop shared standards for identifying training data that is useful beyond superficial correctness.
• Mentor research engineers and promote technical rigor, clear communication, and effective execution.

Conocimientos

Python

Leadership

Research experience

Communication

Experiment design

Herramientas

Docker

Linux

Descripción del empleo

About the Role

Lead the data quality team at an AI and machine learning company focused on reinforcement learning and agent evaluation. You will shape the strategy and systems used to measure and improve training data, helping ensure it is useful for training agents on real-world tasks.

What You'll Do

• Lead development of systems that evaluate tasks across reinforcement learning environments, synthetic data, benchmarks, and specialized workflows.
• Define data quality strategy, establish standards, and design experiments to assess agent outputs.
• Develop scalable methods to validate synthetic data, including failure analysis, task mutation checks, and trajectory audits.
• Work with research engineers, domain experts, and data vendors to diagnose quality issues and improve data generation workflows.
• Turn research insights into production tools, dashboards, validation pipelines, and feedback loops.
• Develop shared standards for identifying training data that is useful beyond superficial correctness.
• Mentor research engineers and promote technical rigor, clear communication, and effective execution.

What We're Looking For

• At least 5 years of research or engineering experience, including building AI or machine learning data evaluation systems.
• Experience leading technical projects or teams through ambiguous problems, from definition to implementation and iteration.
• Advanced proficiency with Python, Docker, and Linux.
• Background in AI research, evaluation, post-training, synthetic data, benchmarks, or data quality infrastructure.
• Strong understanding of what makes agent training data realistic, learnable, diverse, reliable, and useful.
• Experience designing metrics, experiments, and quality assurance processes, and translating research into production systems.
• Experience working with subject-matter experts and converting domain judgment into scalable review or data generation workflows.
• Clear written communication, strong attention to detail, and ability to work independently in an early-stage startup environment.

Compensation & Benefits

Base salary: $130,000 to $225,000 annually. Visa sponsorship is available.

Location

On-site in the United States.