Staff AI Research Scientist, Handshake

• PhD or equivalent experience in machine learning, NLP, or data-centric AI, with a track record of leadership in LLM post-training or data quality research
• 5 years of academic or industry experience post-doc
• Deep expertise in RLHF, preference data pipelines, reward modeling, or evaluation systems
• Demonstrated experience designing and scaling data quality infrastructure — from labeling frameworks and validation metrics to automated filtering and dataset optimization
• Strong engineering proficiency in Python, PyTorch, and ecosystem tools for large-scale training and evaluation
• A proven ability to define, lead, and execute complex research initiatives with clear business and technical impact
• Strong communication and collaboration skills, with experience driving strategy across research, engineering, and product teams

Desirable

• As a Staff Research Scientist, you will play a pivotal role in shaping the future of large language model (LLM) alignment by leading research and development at the intersection of data quality and post-training techniques such as RLHF, preference optimization, and reward modeling
• You will operate at the forefront of model alignment, with a focus on ensuring the integrity, reliability, and strategic use of supervision data that drives post-training performance
• You’ll set research direction, influence cross-functional data standards, and lead the development of scalable systems that diagnose and improve the data foundations of frontier AI
• Lead high-impact research on data quality frameworks for post-training LLMs — including techniques for preference consistency, label reliability, annotator calibration, and dataset auditing
• Design and implement systems for identifying noisy, low-value, or adversarial data points in human feedback and synthetic comparison datasets
• Drive strategy for aligning data collection, curation, and filtering with post-training objectives such as helpfulness, harmlessness, and faithfulness
• Collaborate cross-functionally with engineers, alignment researchers, and product leaders to translate research into production-ready pipelines for RLHF and DPO
• Mentor and influence junior researchers and engineers working on data-centric evaluation, reward modeling, and benchmark creation
• Author foundational tools and metrics that connect supervision data characteristics to downstream LLM behavior and evaluation performance
• Publish and present research that advances the field of data quality in LLM post-training, contributing to academic and industry best practices