Principal Applied Scientist, Generative AI

Fully remote work within the United StatesFull-TimePrincipal

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.

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Job Details

Required Skills: PythonPyTorchLLM

Requirements

PhD in Computer Science, Machine Learning, or a related discipline, or a master's degree supported by equivalent research experience.
Deep, hands-on experience with large language models or foundation models.
Experience with 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).
Strong understanding of reward modeling, preference learning, model evaluation, and experimental methodologies for improving LLM performance.
Ability to lead and develop scientists or engineers while remaining technically hands-on, or to lead a research group with a strong record of scientific contributions and publications.
Ability to independently define research problems, design experiments, analyze results, and turn ambiguous challenges into actionable technical solutions.
Proficiency in Python and machine-learning frameworks such as PyTorch.
Ability to read, debug, review, and guide experimental code.
Strong communication and collaboration skills, including the ability to influence technical direction and explain scientific concepts to technical and non-technical audiences.
Preferred: publications at leading AI and machine-learning conferences such as NeurIPS, ICML, ICLR, ACL, or EMNLP.
Preferred: experience applying LLMs in large-scale, consumer-facing products and translating research innovations into production-ready capabilities.
Familiarity with agentic AI systems and their evaluation, reinforcement learning, and preference-learning approaches is an additional advantage.

Responsibilities

Establish the scientific roadmap for LLM post-training, reward modeling, and evaluation of agentic systems.
Provide technical direction, mentorship, and research guidance to a team of applied scientists.
Define research hypotheses and experimental designs for complex, ambiguous problems.
Develop and refine supervised fine-tuning, preference optimization, and reinforcement-learning approaches.
Establish methods to measure language-model and agentic-system performance, reliability, and effectiveness.
Apply generative AI research to practical product improvements and consumer-facing experiences.
Partner with Product, Engineering, Platform, and Data teams to integrate AI capabilities into production systems.
Present research findings, technical trade-offs, and recommendations to executive leadership and cross-functional stakeholders.
Promote reproducible experimentation and evidence-based decision-making.

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