Community Growth Manager, Contributor Acquisition

At Besimple AI, we’re making it radically easier for teams to build and ship reliable AI by fixing the hardest part of the stack: data. Good evaluation, training and safety data require domain experts, robust tooling and meticulous QA. AI teams and labs come to us to get high quality data so they can launch AI safely. We’re a YC X25 company based in Redwood City, CA, already powering evaluation and training pipelines for leading AI companies across customer support, search, and education. Join now to be close to real customer impact, not just demos.

High-quality, human-reviewed data is still the single biggest driver of model quality, but most teams are stuck with old tools and legacy processes that do not scale to modern, multimodal, agentic workflows. Besimple replaces that mess with instant custom UIs, tailored rubrics, and an end-to-end human-in-the-loop workflow that supports text, chat, audio, video, LLM traces, and more. We meet teams where they are—whether they need on-prem deployments and granular user management or a fast cloud setup—to turn evaluation into a continuous capability rather than a one-time project.

Founders previously built the annotation platform that supported Meta’s Llama models. We’ve seen how world-class annotation systems shape model quality and iteration speed; we’re bringing those lessons to every AI team that needs to ship with confidence. You’ll work directly with the founders and users, owning problems end-to-end—from an interface that unlocks a tough rubric, to a workflow that reduces disagreement, to a AI judge system that improves quality.

• Bias to shipping and learning with customers
• Respect for craft: calibration, rubric clarity, inter-annotator agreement (IRR)
• Tight feedback loops from production back to evaluation
• Ownership: you’ll shape evaluation as an engineering discipline with real “fail-to-ship” tests tied to business and safety goals

If you’re excited by systems that combine product design, human judgment, and applied AI—and you want to build the data and evaluation layer that keeps AI trustworthy—come build with us. See how fast teams can go from raw logs to a robust, human-in-the-loop eval pipeline—and how that changes the way they ship AI.

Besimple AI is building the data and benchmark infrastructure for the next generation of voice AI. We help AI understand people across languages, accents, and real-world conversations. Our founders are former Meta product and engineering leaders from MIT and Brown, and we work directly with frontier AI labs.

We’re building a crowdsourcing platform where people contribute to paid projects that help improve AI. We’re looking for someone to grow its social presence, manage its online community, and find creative ways to recruit new contributors.

You should enjoy making content, starting conversations, and getting people involved. Maybe you’ve grown your own social account, recruited members for a campus organization, built an online community, or found an unexpected way to get people excited about something. We care about what you’ve done and how you think, whether that experience comes from a job or something you built yourself.

In this role, you will:

• Run our platform’s social media accounts. Create and publish content at least daily, engage with comments and questions, and test different formats to understand what resonates.
• Manage our Reddit community. Welcome contributors, encourage useful discussions, answer routine questions, and bring feedback and recurring issues to our team. Help build a community where people feel heard and want to participate.
• Recruit contributors through online channels. Find relevant communities and promote paid opportunities on Reddit, Craigslist, Handshake, campus groups, and other channels. Adapt your messaging to each audience and follow each community’s posting rules.
• Find and test creative growth tactics. Explore new places to reach contributors, try different messages and approaches, and turn successful experiments into repeatable practices.
• Build monitoring and reporting. Set up tools to track conversations and channel performance. Measure engagement, traffic, signups, and—working with our team—which channels bring contributors who complete approved work.
• Bring contributor insights back to the team. Help us understand what attracts people, what confuses them, and what would make them more likely to participate.

We’re especially interested in three things:

• Hands-on social media experience. You’ve actively created content and managed an account. Ideally, you operate your own social presence or have built an audience around a personal interest, project, or community. You can explain what you tried, what worked, and what you learned.
• Experience recruiting or mobilizing people. This could mean recruiting for a company, growing a student club, organizing volunteers, bringing people to events, or getting a community involved in a project. You know how to reach people and turn interest into participation.
• Creative growth instincts. You look beyond the obvious channels and standard playbooks. You can come up with an unconventional idea, explain why it might work, and test it without needing a large budget.

You should also write clearly, use good judgment in public conversations, and follow through consistently. You’re comfortable with spreadsheets and basic analytics, and willing to learn tools that make monitoring, publishing, and reporting easier.

Success means our channels are active and responsive, contributors know where to find opportunities and get help, and we can see which activities bring qualified people onto the platform. You’ll help us build a reliable system for learning what works and doing more of it.

When applying, please share:

• Links to social accounts, communities, or projects you’ve personally managed, with a brief explanation of your contribution.
• An example of how you recruited people or grew participation in something.
• One unconventional tactic you would test to recruit contributors for an AI crowdsourcing platform, and how you would measure whether it worked.

Besimple generates task-specific annotation interfaces and guidelines on the fly, runs human-in-the-loop (HITL) workflows at scale, and trains AI judges that learn from human decisions to triage easy cases and flag ambiguous ones. We support multimodal data (text, chat, audio, video, traces) and enterprise needs like on-prem deployment and fine-grained access control. Under the hood, we optimize for latency, correctness, and adaptability—simultaneously.

• Generative UI for Any Data Shape Turn arbitrary inputs—JSON logs, multi-turn dialogs, code diffs, speech transcripts, video frames—into ergonomic, versioned UIs with validation and assistive affordances (schema inference, promptable components, live preview with safe defaults).
• Human-in-the-Loop Orchestration Route tasks to the right experts, enforce calibration and quality gates, measure IRR, and run adjudication when disagreement is informative—not noise.
• AI-Judge Training & Control Distill human rubrics into model-based evaluators that score live traffic, self-update with new human decisions, and stay inside guardrails (confidence thresholds, policy constraints, auditability).
• Production-Grade Eval Build gating suites and regression tests aligned to product KPIs and safety constraints; snapshot datasets; track drift; and plumb production signals back into evaluation and training.
• Enterprise Delivery On-prem optional installs, isolation-by-tenant, SSO/RBAC, and audit trails that satisfy infosec without slowing iteration.

End-to-end slices of the product—e.g., building a new multimodal interface, designing a calibration workflow that improves IRR, shipping a rubric-aware AI judge for a new domain, or tightening dataset lineage so a customer can trace a production decision back to ground truth.

This work sits at the intersection of product engineering, systems design, and applied AI. You’ll ship tangible interfaces, shape evaluation science, and see your work block real regressions. The feedback loop is measured in better models in production, not vanity benchmarks.