Applied Research Engineer

Role Overview

As an Applied Research Engineer at Labelbox, you will be at the forefront of developing cutting-edge systems and methods to create, analyze, and leverage high-quality human-in-the-loop data for frontier model developers. Your role will involve designing and implementing advanced systems that align human feedback into AI training processes, such as Reinforcement Learning from Human Feedback (RLHF), Direct Preference Optimization (DPO), etc. You will also work on innovative techniques to measure and improve human data quality, and develop AI-assisted tools to enhance the data labeling process. Your expertise in machine learning, frontier model training, and advanced human data alignment techniques will be crucial in pushing the boundaries of AI capabilities and delivering state-of-the-art solutions to meet the evolving needs of our customers.

Your Impact

• Advance the field of AI alignment by developing cutting-edge methods, such as RLHF and novel approaches, that ensure AI systems reflect human preferences more accurately.
• Improve the quality of human-in-the-loop data by designing and deploying rigorous measurement and enhancement systems, leading to more reliable AI training.
• Increase efficiency and effectiveness in AI-assisted data labeling by creating tools that leverage active learning and adaptive sampling, reducing manual effort while improving accuracy.
• Shape the next generation of AI models by investigating how different types of human feedback (e.g., demonstrations, preferences, critiques) impact model performance and alignment.
• Optimize human feedback collection by developing novel algorithms that enhance how AI learns from human input, improving model adaptability and responsiveness.
• Bridge research and real-world application by integrating breakthroughs into Labelbox’s product suite, making human-AI alignment techniques scalable and impactful for users.
• Drive industry innovation by engaging with customers and the broader AI community to understand evolving data needs and share best practices for training frontier models.
• Contribute to the AI research ecosystem by publishing in top-tier journals, presenting at leading conferences, and influencing the future of human-centric AI.
• Stay ahead of AI advancements by continuously exploring new frontiers in human-AI collaboration, human data quality, and AI alignment, keeping Labelbox at the cutting edge.
• Establish Labelbox as a thought leader in AI by creating technical documentation, blog posts, and educational content that shape the industry's approach to human-centric AI development.

What You Bring

• A strong foundation in AI and machine learning, backed by a Ph.D. or Master’s degree in Computer Science, Machine Learning, AI, or a related field.
• Proven experience (3+ years) in solving complex ML challenges and delivering impactful solutions that improve real-world AI applications.
• Expertise in designing and implementing data quality measurement and refinement systems that directly enhance model performance and reliability.
• A deep understanding of frontier AI models—such as large language models and multimodal models—and the human data strategies needed to optimize them.
• Proficiency in Python and experience with deep learning frameworks like PyTorch, JAX, or TensorFlow to prototype and develop cutting-edge solutions.
• A track record of publishing in top-tier AI/ML conferences (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL) and contributing to the broader research community.
• The ability to bridge research and application by interpreting new findings and rapidly translating them into functional prototypes.
• Strong analytical and problem-solving skills that enable you to tackle ambiguous AI challenges with structured, data-driven approaches.
• Exceptional communication and collaboration skills, allowing you to work effectively across multidisciplinary teams and with external stakeholders.

Labelbox Applied Research

At Labelbox Applied Research, we're committed to pushing the boundaries of AI and data-centric machine learning, with a particular focus on advanced human-AI interaction techniques. We believe that high-quality human data and sophisticated human feedback integration methods are key to unlocking the next generation of AI capabilities. Our research team works at the intersection of machine learning, human-computer interaction, and AI ethics to develop innovative solutions that can be practically applied in real-world scenarios.

We foster an environment of intellectual curiosity, collaboration, and innovation. We encourage our researchers to explore new ideas, engage in open discussions, and contribute to the wider AI community through publications and conference presentations. Our goal is to be at the forefront of human-centric AI development, setting new standards for how AI systems learn from and interact with humans.