Strategic Project Lead - AI Post-Training (United States)

About the Role

The Strategic Project Lead – AI Post-Training will help lead AI companies develop and improve frontier models through high-quality data programs. We’re looking for someone with experience in AI post-training, preference data, or model evaluation to lead projects that shape how models respond, reason, and perform. 

You’ll own delivery while bringing practical knowledge of the data, evaluation methods, and quality standards behind model improvement. Working closely with clients and internal teams, you’ll help translate evolving AI research needs into clear workflows, reliable datasets, and measurable outcomes. 

• Own end-to-end project delivery, including scope, timelines, budgets, staffing, and risk management. 
• Lead client communication, status reporting, planning sessions, and escalations. 
• Translate model improvement objectives into data requirements, workflows, rubrics, and evaluation plans. 
• Define quality standards and acceptance criteria for preference data, model comparisons, and evaluation outputs. 
• Monitor delivery quality, investigate inconsistencies, and resolve ambiguous requirements with clients. 
• Advise on data design, evaluation approaches, and project feasibility based on your domain expertise. 
• Develop reusable guidelines, SOPs, and QA frameworks to improve consistency across programs. 
• Mentor Operations Specialists and contribute to project scoping, pilots, and new capability development. 

• 1–2+ years of experience in AI data operations, project coordination, or delivery, ideally in a GenAI or frontier AI environment.
• Familiarity with at least one AI post-training or model evaluation workflow. 
• Experience coordinating projects or workstreams, managing stakeholders, and communicating with clients.
• Understanding of data quality, annotation workflows, and quality assurance methods.
• Ability to translate evolving requirements into clear workflows, rubrics, and measurable quality standards. 
• Strong troubleshooting and organizational skills, with the ability to manage multiple priorities. 
• Interest in current AI developments, including frontier models and post-training approaches. 

• Experience with reinforcement learning from human feedback (RLHF), preference data, or model response evaluation.
• Familiarity with rubric design, model comparison, benchmark development, or evaluation creation.
• Experience assessing model responses for quality, accuracy, reasoning, or instruction following.
• Exposure to AI companies, frontier AI labs, or model evaluation programs.
• Bilingual capabilities, particularly German or a Nordic language.