Strategic Project Lead - AI Post-Training
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AppenAI data
Remote, LondonFull-TimeLead
Salary not disclosed
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Job Details
- Languages
- Bilingual capabilities, particularly German or a Nordic language.
- Experience
- 1–2+ years of experience in AI data operations, project coordination, or delivery
- Required Skills
- Project CoordinationQuality AssuranceStakeholder management
Requirements
- Have 1–2+ years of experience in AI data operations, project coordination, or delivery, ideally in a GenAI or frontier AI environment.
- Be familiar with at least one AI post-training or model evaluation workflow.
- Have experience coordinating projects or workstreams, managing stakeholders, and communicating with clients.
- Understand data quality, annotation workflows, and quality assurance methods.
- Be able to translate evolving requirements into clear workflows, rubrics, and measurable quality standards.
- Have troubleshooting and organizational skills, with the ability to manage multiple priorities.
- Bring an interest in current AI developments, including frontier models and post-training approaches.
- Experience with RLHF, preference data, or model response evaluation is a nice-to-have.
- Familiarity with rubric design, model comparison, benchmark development, or evaluation creation is a nice-to-have.
- Experience assessing model responses for quality, accuracy, reasoning, or instruction following is a nice-to-have.
- Exposure to AI companies, frontier AI labs, or model evaluation programs is a nice-to-have.
- Bilingual capabilities, particularly German or a Nordic language, are a nice-to-have.
Responsibilities
- 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 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.
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