Leads end-to-end AI post-training and model evaluation projects, managing scope, timelines, budgets, staffing, risks, clients, and delivery quality. Translates model improvement goals into data requirements, workflows, rubrics, evaluation plans, and acceptance criteria. Investigates quality issues, develops SOPs and QA frameworks, advises on data and evaluation design, mentors operations specialists, and supports project scoping and capability development.
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.
Your Impact
- 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.
What You Bring
- 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.
Nice to Have
- 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.
Why You'll Love Working Here
At Appen, we foster a culture of innovation, collaboration, and excellence. We value curiosity, accountability, and a commitment to delivering the highest quality AI solutions for frontier models.
You'll work on complex challenges that shape the future of AI across industries and geographies, alongside talented people in a culture that values humility over ego. You'll have the flexibility to deliver in a way that works for you and your team, supported by tools, resources, and development opportunities to continue to build your capability over time.
About Appen
Appen has been a leader in AI training data for over 30 years. We specialise in human generated data to train, fine tune, and evaluate models across generative AI, large language models, computer vision, and speech recognition. Our AI assisted data annotation platform and global crowd of more than 1 million contributors in over 200 countries support model pre training, supervised fine tuning, evaluation and benchmarking, safety and red teaming, and multilingual global expansion.
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