Talent Engineer
SuperAnnotate AI
Talent Engineer
SuperAnnotate AI
End-to-end data infrastructure for AI datasets
Salary Range: 130000 to 170000 (Currency: USD) (Pay period: per-year-salary)
SME Careers builds the global expert workforce behind every SuperAnnotate project, bringing together specialists across languages and professional fields, from medicine and law to software engineering and AI safety. We're building a recruiting engine that finds and engages the right experts anywhere in the world, as fast as new projects need them.
As our first Talent Engineer, you'll design and own that engine. You'll choose and configure the AI sourcing and outreach tools we use, build the workflows that connect them to our platform, and turn them into systems that run reliably at scale. You'll approach recruiting as a systems and data problem, building an engine that grows faster than the team running it. You'll measure what works, cut what doesn't, and keep improving as our projects evolve.
You'll work directly with our Director of Product and Operations and the SME Careers talent team, and alongside engineering whenever an integration needs building. You'll understand recruiting well enough to know what good outreach and a good candidate look like, and you'll be technical enough to build the tooling yourself rather than waiting on someone else to.
This is a full-time, hybrid position based in San Francisco.
Own the AI sourcing stack: Configure the AI sourcing and outreach platforms and agents we use, and own their performance once they're live.
Build workflows that scale: Connect our job platform to sourcing tools so every new role is sourced automatically from day one.
Own screening quality: Build and maintain the prompts, evaluation logic, and quality checks behind AI-assisted screening, so the strongest experts reach a human first.
Run outbound at volume: Design reusable outreach sequences across email and LinkedIn, A/B test messaging and steps, and keep what converts.
Make the funnel measurable: Build reporting that gives real-time visibility into source quality, conversion, time to staff, and cost per active expert, and report it honestly enough to change decisions.
Rediscover talent we already have: Set up workflows that search our existing applicant pool before we spend on new candidates.
Partner with engineering: Scope and build integrations through APIs and webhooks, and work with engineers when a change needs to land in our platform.
Bring recruiting judgment to automation: Calibrate search criteria with the talent team, protect the candidate experience, and keep outreach compliant with privacy rules like GDPR.
Level up the team: Document the architecture and workflows you build, and train the talent team to run and adjust them independently.
3+ years in recruiting, sourcing, or recruiting operations, with a working understanding of how sourcing, screening, and outreach fit together.
Experience recruiting at high volume, especially contractor, freelance, or gig workforces.
Hands-on experience running AI sourcing or outreach tools, such as Juicebox, hireEZ, Gem, or similar, beyond basic searches.
Programming or scripting experience, ideally Python or JavaScript, plus SQL, and comfort working with REST APIs, JSON, and webhooks.
A track record of building automations or workflows that other people rely on and that run without daily hand-holding, with attention to data integrity and long-term maintainability.
Hands-on experience building software with AI coding tools like Claude Code, Cursor, or Codex, beyond chat-based tools like ChatGPT. You’ve shipped working tools or automations this way, not just experimented.
Comfort with data: you can build a funnel report from raw exports or an API and explain what it means.
You understand what a recruiter's day actually looks like, and you build tools a team will actually adopt.
Good judgment about candidate experience, and the instinct to know when outreach reads as automated.
Comfort with ambiguity and building a function from scratch.
Experience building recruiting or HR technology, or working at a company that builds it.
Background in AI training data, data annotation, or expert networks.
Experience with workflow automation tools such as n8n, Make, or Zapier.
Global recruiting experience across multiple regions and languages.
A computer science, data, or engineering background that led into recruiting or people operations.
This role doesn't exist anywhere else in the company yet, and you'd be defining it. The tools, workflows, and playbook you build will decide how SME Careers finds the experts behind every project we deliver, across every language and field we hire in.
You'll have real ownership and real visibility: a direct line to the leader running SME Careers, play a key part in selecting the tools, building what works, and owning results that show up in our ability to staff projects quickly. If you've ever wanted to build recruiting infrastructure rather than just operate it, this is that chance.
End-to-end data infrastructure for AI datasets
SuperAnnotate provides an end-to-end data infrastructure platform for creating, managing, and evaluating high-quality AI training datasets, combining a SaaS platform with managed annotation services. It supports multimodal data (images, video, text, and audio) and offers workflow automation, team management, and quality control that fit with a company’s existing data sources and model pipelines. It differentiates itself by pairing enterprise-grade data tooling with a global network of vetted annotators for scalable, human-in-the-loop labeling, rather than relying on generic labeling tools. Its goal is to help enterprises build and deploy high-quality AI models faster by unifying data creation, curation, and evaluation in one platform.
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