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Strategic Project Lead – Speech & Audio AI (United States)

AI Summary
This role leads end-to-end delivery of AI data programs focused on speech, audio, and conversational AI. The Project Lead translates client requirements into annotation workflows, quality standards, tooling, and operational plans. Key responsibilities include managing timelines, budgets, staffing, risks, client communications, and escalations while monitoring measures such as transcription accuracy, WER, and diarization quality. The position also develops reusable SOPs and QA frameworks, mentors Operations Specialists, and contributes to new project scoping and pilots. It is a US-remote full-time role requiring strong project coordination and practical familiarity with speech or language-data workflows.
Remote from
USA
Salary
Undisclosed
Employment
Full Time
Experience
Senior
Published
Apply before
9 Dec 2026
Listing views
25
Application actions
1
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Opportunity details

About this 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.

Compensation

Additional Information

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This job listing has been manually reviewed by the Jobicy Trust & Safety Team for compliance with our posting guidelines, including verification of the company's legitimacy, accuracy of job details, clarity of remote work policy, and absence of misleading or fraudulent content.

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 insightThe role combines client-facing project ownership with specialized speech-data quality knowledge and simultaneous responsibility for delivery, staffing, risks, and ambiguous requirements. Rapidly evolving AI workflows and quality metrics increase the operational complexity.

Salary analysis

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

Estimated job median (per year)Market rate
$90,000
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. The annual cadence and USD market estimate are inferred for a full-time US remote project-lead role in AI data operations; estimated market range is $75,000–$115,000 per year, with an estimated midpoint of $90,000 per year.

Core skills

Skills and capabilities most closely associated with this opportunity.

Sample interview questions
How would you turn a client's speech-data requirement into an annotation workflow?

I would clarify the intended model use case, target languages, audio sources, edge cases, acceptance criteria, and delivery constraints. I would then create actionable guidelines, examples, decision trees, tooling instructions, a pilot plan, and measurable QA thresholds before scaling the workflow.

How would you investigate a decline in transcription accuracy or an increase in WER?

I would first validate the metric calculation and segment the data by annotator group, language, audio quality, domain, and error type. I would review samples, identify root causes such as unclear guidance or tool issues, implement corrective training or guideline changes, and monitor the next quality batch for improvement.

Describe how you would manage a client escalation caused by ambiguous requirements.

I would acknowledge the issue, document the specific ambiguity and delivery impact, and arrange a focused decision session with the relevant client and internal stakeholders. After agreeing on an interpretation, I would update the requirements, guidelines, and change log, communicate the decision to the team, and assess any needed rework or timeline changes.

What metrics would you use to manage quality in a speaker-diarization project?

I would use diarization error rate and its components, including missed speech, false alarm speech, and speaker confusion, alongside inter-annotator agreement and audit pass rates. I would also monitor performance by audio condition, speaker count, language, and difficult acoustic scenarios to pinpoint operational issues.

How do you balance speed, budget, and quality on a fast-moving AI data project?

I establish explicit quality thresholds and prioritize the data segments that most affect model outcomes. I use pilots, sampling-based QA, capacity planning, transparent risk reporting, and staged releases so stakeholders can make informed trade-offs without compromising critical quality requirements.

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