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Applied Research Engineer
| Company | Labelbox |
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| Location | San Francisco, CA, USA |
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| Salary | $250000 – $300000 |
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| Type | Full-Time |
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| Degrees | Master’s, PhD |
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| Experience Level | Mid Level, Senior |
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Requirements
- 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.
Responsibilities
- 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.
Preferred Qualifications
No preferred qualifications provided.
