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Strategic Project Lead – Multimodal AI (London)

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Remote from
UK
Salary
Undisclosed
Employment
Full Time
Experience
Senior
Published
Apply before
27 Nov 2026
Listing views
23
Application actions
2
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AI Summary

The role, at a glance.

Appen is seeking a Strategic Project Lead to deliver multimodal AI data programs for frontier AI clients. The role owns project scope, timelines, budgets, staffing, risks, client communication, and delivery escalations. It requires practical familiarity with image, video, 3D, sensor, robotics, or related data-annotation workflows, alongside strong quality-management skills. The lead will translate technical requirements into collection workflows, annotation guidance, acceptance criteria, and reusable QA processes. This is a remote UK-based full-time role in a fast-moving AI data operations environment.

Role DNA

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

Job Complexity

4/5
EasyHard

Pace & Pressure

5/5
RelaxedFast-paced

Autonomy Level

4/5
GuidedFull ownership

Communication Load

5/5
IndependentCollaborative
AI insightThe role combines end-to-end delivery accountability with specialized multimodal data knowledge and direct client management. Ambiguous technical requirements, quality risks, and rapidly changing AI workflows increase its complexity and pace.

Salary analysis

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

Estimated job median (per year)Highly competitive
$90,000
US market range (per year)$70k–$110k
AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. No employer salary was disclosed. The figures are non-verified annual USD estimates for a UK-based strategic project lead with multimodal AI data delivery responsibilities; the yearly period is inferred from the full-time professional role.

Core skills

Skills and capabilities most closely associated with this opportunity.

Sample interview questions
How would you turn an ambiguous client request for multimodal training data into an executable delivery plan?

I would first clarify the intended model use case, data modalities, volume, quality thresholds, edge cases, and acceptance criteria. I would then create a scoped workflow covering collection or sourcing, annotation instructions, tooling, sampling, QA, staffing, milestones, risks, and escalation paths, validating assumptions with the client before launch.

What quality metrics would you define for an image or video annotation project?

I would establish measurable metrics such as inter-annotator agreement, gold-set accuracy, completeness, label consistency, ontology adherence, turnaround time, and defect severity. The exact thresholds would be tied to the model use case and monitored through routine audits and targeted rework loops.

Describe how you would manage a project that is falling behind schedule because of quality failures.

I would quantify the failure patterns, isolate whether the root cause is instructions, tooling, source data, training, or capacity, and immediately communicate the impact and recovery options. I would prioritize fixes to the highest-risk workstreams, update the plan and staffing, run a controlled pilot to confirm improvement, and provide transparent client status reporting.

How do you communicate technical annotation or data-quality issues to non-technical stakeholders?

I translate the issue into its business and delivery impact, using concise examples and clear options rather than jargon. I explain what is affected, the decision required, the risks of each option, and my recommended path, while keeping technical detail available for specialists.

What experience or approach would you bring to projects involving 3D, sensor, or robotics data?

I would focus on understanding the data schema, capture conditions, synchronization requirements, annotation ontology, and downstream evaluation needs before defining the workflow. For complex modalities, I would use pilots, detailed edge-case guidance, specialist QA sampling, and regular feedback with domain experts to ensure labels remain useful and consistent.

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 – Multimodal AI supports frontier AI development through complex, high-quality data programs for leading AI companies. We’re looking for someone with experience in multimodal AI data, such as image, video, 3D, sensor, or robotics-related workflows.

You’ll lead projects that turn complex real-world data into reliable training and evaluation resources. Combining project management with hands-on domain knowledge, you’ll partner with clients and internal teams to design workflows, maintain quality, and solve challenging data delivery problems in a rapidly evolving AI market.

Your Impact

  • Own end-to-end project delivery across scope, timelines, budgets, staffing, and risks.
  • Lead client communication, status reporting, planning sessions, and escalations.
  • Translate technical and client requirements into data collection workflows, annotation guidelines, tooling, and quality standards.
  • Define quality metrics and acceptance criteria for multimodal data and annotation outputs.
  • Monitor delivery quality, troubleshoot data issues, and resolve ambiguous requirements with clients.
  • Advise on project feasibility, collection methods, annotation approaches, and tooling based on your domain expertise.
  • Build reusable SOPs, guidelines, and QA frameworks to support consistent delivery.
  • Mentor Operations Specialists and contribute to project scoping, pilots, and emerging multimodal AI capabilities.

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 multimodal data workflow involving images, video, 3D data, sensors, or robotics.
  • Experience coordinating projects or workstreams, managing stakeholders, and communicating with clients.
  • Understanding of data quality, annotation workflows, and quality assurance methods.
  • Ability to translate project requirements into clear data collection workflows, annotation guidelines, and quality standards.
  • Strong problem-solving skills and the ability to manage multiple priorities in a fast-moving environment.
  • Interest in current AI developments, including multimodal models, spatial AI, or embodied AI.

Nice to Have

  • Experience with image or video annotation, 3D data, point clouds, or sensor data.
  • Familiarity with robotics data collection or real-world perception workflows.
  • Exposure to 2D or 3D labeling, segmentation, tracking, or structured annotation tasks.
  • Experience with autonomous systems, spatial AI, or embodied AI.
  • 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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