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Appen

Strategic Project Lead - Speech & Audio AI (London)

London, England, United Kingdom

Location
London, England, United Kingdom
Work
Remote
Employment
Full-time
Level
Lead / staff
Function
Operations
Pay
Not stated
Posted
10 days ago

About the role

About the Role

The Strategic Project Lead – Speech & Audio will sit in Appen's Operations organization and support the development of frontier AI through high-quality data programs for leading AI companies. We’re looking for someone with experience in speech, audio, or language data to own complex projects from planning through delivery.

You’ll combine project management, domain expertise, and quality ownership to help clients build and improve speech recognition systems, voice agents, and other AI applications. You’ll work closely with clients and internal teams to solve problems, refine workflows, and keep pace with a rapidly evolving AI landscape.

Your Impact

  • Own end-to-end delivery across project scope, timelines, budgets, staffing, and risks.
  • Manage client communication, status updates, workshops, and escalations.
  • Translate client requirements into annotation guidelines, workflows, tooling, and quality standards.
  • Define and monitor quality metrics, including transcription accuracy, word error rate (WER), diarization, and linguistic consistency.
  • Troubleshoot quality issues and resolve ambiguous requirements with clients.
  • Provide domain expertise across speech and audio workflows, advising on feasibility, evaluation methods, and tooling.
  • Build reusable guidelines, SOPs, and QA frameworks while mentoring Operations Specialists.
  • Support new project scoping, pilots, and capability development as speech and voice AI evolves.

What You Bring

  • 1–2+ years of experience in AI data operations, project coordination, or delivery, ideally in a GenAI or frontier AI environment.
  • Hands-on familiarity with at least one speech, audio, language, or conversational AI workflow.
  • Experience coordinating projects or workstreams, managing stakeholders, and communicating with clients.
  • Understanding of data quality, annotation workflows, and quality assurance methods.
  • Ability to turn client requirements into clear guidelines, workflows, and quality standards.
  • Strong problem-solving skills and the ability to manage multiple priorities in a fast-moving environment.
  • Interest in current AI developments and how speech, audio, and voice-agent technologies are evolving.

Nice to Have

  • Experience with ASR transcription, speaker diarization, acoustic annotation, or TTS evaluation.
  • Familiarity with voice-agent scripts, IVR, dialogue design, turn-taking, or conversational evaluation.
  • Experience with localization, cross-locale consistency, CJK languages, or low-resource languages.
  • Bilingual or multilingual 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.

Source

Posted by Appen on their own hiring page, jobs.lever.co. OptiTalent shows it as published, read 07 Nov 2026 03:21 UTC; the original is the reference.

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