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Strategic Project Lead – AI Post-Training (London)

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Remote from
UK
Salary
Undisclosed
Employment
Full Time
Experience
Senior
Published
Apply before
27 Nov 2026
Listing views
13
Application actions
0
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AI Summary

The role, at a glance.

Appen is seeking a Strategic Project Lead to deliver AI post-training and model-evaluation data programs for AI clients. The role owns project scope, schedules, budgets, staffing, risks, client communication, and escalations. It translates model-improvement goals into annotation workflows, rubrics, evaluation plans, quality criteria, and reusable operating procedures. Candidates need practical exposure to AI data operations, model evaluation or preference-data workflows, and cross-functional project coordination. Experience with RLHF, benchmark design, response evaluation, and German or Nordic languages is advantageous.

Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

Job Complexity

4/5
EasyHard

Pace & Pressure

4/5
RelaxedFast-paced

Autonomy Level

4/5
GuidedFull ownership

Communication Load

5/5
IndependentCollaborative
AI insightThis role combines client-facing project leadership with specialized knowledge of AI post-training, data-quality operations, and model evaluation. Ambiguous and evolving AI research requirements create substantial delivery, quality, and stakeholder-management complexity.

Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job median (per year)Highly competitive
$95,000
US market range (per year)$75k–$115k
AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. No employer salary was disclosed. Estimated annual compensation is shown in USD for a project lead with AI data operations and model-evaluation expertise; this is an AI market estimate, not employer-provided pay.

Core skills

Skills and capabilities most closely associated with this opportunity.

Sample interview questions
How would you translate a client's model-improvement objective into an executable data program?

I would first clarify the target behavior, success metrics, model constraints, and intended use cases. I would then define data requirements, task instructions, rubrics, sampling methods, quality thresholds, and an evaluation plan, validating the approach through a pilot before scaling delivery.

Describe how you would maintain quality in a preference-data or model-evaluation project.

I would establish clear acceptance criteria and examples, train contributors, and use calibration exercises before production. During delivery, I would monitor agreement rates, error patterns, and rubric adherence, then investigate discrepancies through audits, feedback loops, and targeted guideline updates.

How do you manage ambiguous requirements from a client working on an evolving AI product?

I document assumptions, identify decisions that affect scope or quality, and convert open questions into specific options with trade-offs. I use regular planning sessions and written decision logs so the client and delivery team remain aligned as requirements evolve.

What would you include in a project risk plan for an AI evaluation program?

I would cover risks related to unclear rubrics, insufficient contributor capacity, data privacy, inconsistent quality, shifting client priorities, and timeline dependencies. Each risk would have an owner, likelihood and impact assessment, early indicators, mitigation actions, and an escalation path.

How would you report project status to a client and internal leadership?

I would provide a concise view of delivery progress against milestones, completed volume, quality metrics, budget and staffing status, key risks, decisions needed, and next steps. Reporting would be tailored to the audience while maintaining a consistent source of truth for the project team.

This analysis is generated from the job description. Salary estimates, role characteristics and sample answers are guidance, not employer-provided facts.
Opportunity details

About this role.

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.

Compensation

Additional Information

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