Applied Research Engineer
Develops advanced systems for human-in-the-loop AI data alignment using RLHF/DPO, improves data quality, and builds AI-assisted labeling tools. Requires Master's/PhD, 3+ years ML experience, Python/PyTorch proficiency, and top-tier publications.
Responsibilities
- Advance AI alignment by developing methods like RLHF and novel approaches to ensure AI systems reflect human preferences.
- Improve human-in-the-loop data quality through measurement and enhancement systems.
- Create AI-assisted data labeling tools using active learning and adaptive sampling.
- Investigate impacts of human feedback types (demonstrations, preferences, critiques) on model performance.
- Optimize human feedback collection with novel algorithms.
- Integrate research breakthroughs into Labelbox’s product suite.
- Engage with customers and AI community, publish in top conferences, and create technical content.
Requirements
- Ph.D. or Master’s in Computer Science, Machine Learning, AI, or related field.
- 3+ years experience solving complex ML challenges with real-world impact.
- Expertise in data quality measurement and refinement systems.
- Deep understanding of frontier AI models (LLMs, multimodal) and human data strategies.
- Proficiency in Python and deep learning frameworks (PyTorch, JAX, TensorFlow).
- Track record of publishing in top AI/ML conferences (NeurIPS, ICML, ICLR, etc.).
- Ability to bridge research to prototypes, strong analytical/problem-solving skills.
- Exceptional communication and collaboration skills.
Compensation
Annual base salary range: $250,000—$300,000 USD (varies by skills, experience, location; excludes equity/benefits).
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