Principal Applied Scientist, Generative AI

Accountabilities: Define AI research strategy: Establish the scientific roadmap for LLM post-training, reward modeling, and evaluation of agentic systems, identifying high-impact opportunities to advance model capabilities. Lead and mentor scientific talent: Provide technical direction, mentorship, and research guidance to a team of applied scientists, fostering scientific rigor, innovation, and continuous learning. Drive end-to-end research initiatives: Independently tackle complex, ambiguous problems, translating product vision into research hypotheses, experimental designs, model improvements, and the data required to achieve meaningful results. Advance LLM performance: Develop and refine post-training approaches, including supervised fine-tuning, preference optimization, reinforcement learning from human feedback, and related techniques, to improve model quality and behavior. Build effective evaluation frameworks: Establish rigorous methods for measuring the performance, reliability, and effectiveness of language models and agentic AI systems, using experimental findings to guide further development. Translate research into production: Apply state-of-the-art generative AI research to practical product improvements, ensuring scientific advances deliver tangible value in consumer-facing experiences. Collaborate across disciplines: Partner with Product, Engineering, Platform, and Data teams to align research priorities with business objectives and successfully integrate AI capabilities into production systems. Communicate scientific insights: Present research findings, technical trade-offs, strategic recommendations, and long-term opportunities clearly to executive leadership and cross-functional stakeholders. Shape technical excellence: Promote reproducible experimentation, sound research practices, and evidence-based decision-making while helping establish the team's long-term technical direction. Requirements Education: PhD in Computer Science, Machine Learning, or a related discipline, or a master's degree supported by equivalent research experience. Applied science expertise: Deep, hands-on experience with large language models or foundation models, particularly in post-training techniques such as supervised fine-tuning (SFT), Direct Preference Optimization (DPO), reinforcement learning from human feedback (RLHF), reinforcement fine-tuning (RFT), or Group Relative Policy Optimization (GRPO). Reward modeling and evaluation: Strong understanding of reward modeling, preference learning, model evaluation, and experimental methodologies for improving LLM performance. Technical leadership: Demonstrated ability to lead and develop other scientists or engineers while remaining technically hands-on, or to lead a research group with a strong record of scientific contributions and publications. Research execution: Proven ability to independently define research problems, design experiments, analyze results, and turn ambiguous challenges into actionable technical solutions. Programming and machine learning: Proficiency in Python and machine learning frameworks such as PyTorch, including the ability to read, debug, review, and guide experimental code. Strategic influence: Strong communication and collaboration skills, with the ability to influence technical direction across teams and explain complex scientific concepts to both technical and non-technical audiences. Preferred research credentials: Publications at leading AI and machine learning conferences, such as NeurIPS, ICML, ICLR, ACL, or EMNLP. Preferred product experience: Experience applying LLMs in large-scale, consumer-facing products and translating research innovations into production-ready capabilities. Additional advantages: Familiarity with agentic AI systems and their evaluation, reinforcement learning, and preference-learning approaches. Working style: Intellectual curiosity, sound scientific judgment, a strong sense of ownership, and the ability to thrive in a fast-evolving environment where research priorities and technical approaches continue to develop. Candidates whose experience does not match every preferred qualification are encouraged to apply if they bring relevant research expertise, strong technical leadership, and a passion for advancing generative AI. Benefits Competitive base salary: $195,600–$312,400 annually in California, Connecticut, Maryland, Massachusetts, New Jersey, New York, Washington State, and Washington, D.C. $185,800–$296,800 annually in Colorado, Hawaii, Illinois, Maine, Minnesota, Nevada, Ohio, Rhode Island, Vermont, and Virginia. Salary ranges vary by location. Actual compensation depends on factors such as experience, performance, and geographic location. Equity awards: Eligibility for equity compensation in addition to base salary, subject to applicable factors and award terms. Remote flexibility: Fully remote work within the United States, subject to limited location restrictions. Collaborative environment: Work alongside applied scientists, engineers, and cross-functional partners on ambitious AI initiatives with substantial technical impact. Research and innovation: Opportunities to work with advanced LLMs, reinforcement learning, post-training techniques, and agentic AI systems at significant scale. Professional development: Opportunities to mentor scientific talent, influence research strategy, and shape the future direction of AI-powered consumer products. Inclusive workplace: A commitment to equal employment opportunity and an environment that values diverse backgrounds, perspectives, and experiences. Accessibility and accommodations: Reasonable accommodations are available for qualified applicants and employees who require them.