BeatpulseLabs
Human Data Manager

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About BeatpulseLabs
Beatpulse Labs builds high-quality human data for some of the world's most demanding AI applications. We run large-scale data programs across video, voice, music, and other multimodal datasets, combining global contributor networks, domain expertise, and rigorous quality systems to deliver training and evaluation data at scale.
As we grow, we're looking for exceptional operators who've built or run complex human-data programs and want end-to-end ownership of a major vertical.
The Role
You'll own the execution and scaling of human-data programs, from client brief through workflow design, contributor recruitment, production, QA, and delivery. This is hands-on and high-ownership: you won't just manage projects, you'll build the operating systems that let hundreds or thousands of contributors and experts consistently produce quality data at scale. We value deep expertise in one modality over shallow experience across many. Tell us where you're strongest: video, voice/audio, music, or another human-data vertical.
What You'll Own
- End-to-end operations. Turn complex or ambiguous client requirements into scalable production workflows. Design annotation, collection, evaluation, and QA processes, and build playbooks that hold up as volumes climb.
- Workforce at scale. Recruit, qualify, and structure distributed networks of annotators, reviewers, and experts. Forecast capacity against deadlines and keep improving quality, reliability, and throughput.
- Team management at scale. Manage teams of thousands or tens of thousands of people, with clear structures, accountability, and operating rhythms that maintain quality and throughput at scale.
- Quality & delivery. Own the quality of the data that reaches the client. Set acceptance criteria, build QA systems, and catch systematic failures before they hit delivery.
- Client delivery. Turn evolving client requirements into executable programs alongside commercial teams. Communicate risks and trade-offs clearly, and own delivery against quality, timeline, and commercial expectations.
- Metrics & unit economics. Track throughput, acceptance rates, productivity, turnaround, cost per accepted unit, and margin, and use them to clear bottlenecks and build sustainable economics.
- Tooling & automation. Partner with product and engineering to sharpen contributor tooling, automate the repetitive, and design systems for far larger volumes and populations.
Reasons to use Rodeo
I’m in my final year doing Economics and I don’t know whether to apply for grad schemes now or do a masters first. What do you think?
Honest answer — it depends on where you want to end up. A lot of top grad schemes (Big 4, civil service, banking) don’t need a masters. Let’s look at the ones you’d be competitive for now, and we can decide if a masters actually adds anything.
Also worth knowing: most autumn 2026 applications are open now. Timing matters more than you think.
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Graduate Consultant — 2026 Scheme
Why you're a good match
StrongYour economics background and your summer at a regional bank line up with what PwC looks for on the consulting scheme. Applications close in four weeks.
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Why you're a good match
You’ve got the grades and the economics background, and your bank internship is exactly the experience this scheme looks for. Apply soon — deadlines close within the month.
Experience fit
Your summer at the bank plus your econometrics coursework map directly to the day-one responsibilities on this scheme — client modelling, market briefings, and deal support.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
What We're Looking For
- 5+ years in human data, AI training data, annotation, AI evaluation, marketplace ops, or a related field, or equivalent experience running complex programs at scale.
- Direct ownership of high-volume human-data programs, ideally spanning hundreds or thousands of distributed contributors or experts.
- Experience designing annotation, evaluation, data collection, or QA workflows.
- A strong grasp of the trade-offs between quality, throughput, capacity, speed, and cost.
- Strong analytical and operational problem-solving, comfortable in fast-moving environments and moving between strategy and hands-on execution without a large team beneath you.
- Strong client-facing and communication skills.
- Ownership and a hustler mentality. When something breaks you find the root cause and fix it; you're scrappy, biased toward action, and don't wait for permission or perfect information.
Experience at a scaled AI-data company (Surge AI, Mercor, Scale AI, Handshake AI, micro1, or equivalent) is strongly preferred, especially launching programs from scratch, building qualification and QA systems, improving acceptance rates, or cutting cost per accepted unit, working directly with AI labs, or owning project P&L.


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Modalities We're Hiring Across
- Video: annotation, collection, scene description, visual understanding, action recognition, multimodal video.
- Voice / Audio: speech collection, transcription, recording, audio annotation, speech evaluation, conversational data.
- Music: music annotation, expert evaluation, composition-related datasets, music understanding, other specialist music data.
- Other human data: exceptional operators from other verticals who've built and scaled complex AI training or evaluation programs.
What Success Looks Like
Within your first few months, you can take a new client requirement and independently build the operating system to deliver it: the right contributors, task and workflow design, QA, capacity, metrics, and economics. You'll know where an operation breaks at 100, 1,000, and 10,000 contributors, and how to get ahead of it. Ultimately, we want people who can own a major human-data vertical at Beatpulse and build it into a world-class operation.
Who Will Thrive Here
You'll enjoy Beatpulse if you prefer building systems over inheriting them, make good calls with incomplete information, and move fast without dropping quality. You're highly analytical but just as comfortable with people, you care about both client outcomes and project economics, and you want to build the human-data infrastructure behind the next generation of AI.
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