Applied Research Engineer

Job Description

Labelbox is seeking an Applied Research Engineer to develop cutting-edge systems for creating and leveraging high-quality human-in-the-loop data. The role involves designing and implementing advanced systems that align human feedback into AI training processes, such as Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO). The engineer will also work on innovative techniques to measure and improve human data quality, and develop AI-assisted tools to enhance the data labeling process.

Responsibilities:

• Advance the field of AI alignment.
• Improve the quality of human-in-the-loop data.
• Increase efficiency and effectiveness in AI-assisted data labeling.
• Shape the next generation of AI models.
• Optimize human feedback collection.
• Bridge research and real-world application.
• Drive industry innovation.
• Contribute to the AI research ecosystem.
• Stay ahead of AI advancements.
• Establish Labelbox as a thought leader in AI.

Requirements:

• A strong foundation in AI and machine learning.
• Proven experience (3+ years) in solving complex ML challenges.
• Expertise in designing and implementing data quality measurement and refinement systems.
• A deep understanding of frontier AI models.
• Proficiency in Python and experience with deep learning frameworks like PyTorch, JAX, or TensorFlow.
• A track record of publishing in top-tier AI/ML conferences.
• The ability to bridge research and application.
• Strong analytical and problem-solving skills.
• Exceptional communication and collaboration skills.

Labelbox offers:

• Hybrid work model.
• Career advancement opportunities.