Besimple AI
Voice data for AI
Founding Engineer (Full-Stack)
About the role
About Besimple
Besimple AI is a YC-backed startup from X25 batch. We provide training and evaluation data to AI labs, starting with audio. Our mission is to bring AI into the real world safely. We believe that AI can meaningfully empower humanity only if we put safety first. We’re a small, nimble team of passionate builders who believe humans must remain in the loop. With Besimple AI, you get to work with all parts of AI data, from collection to curation, you will learn how to build highest quality dataset that can extend model capabilities for tomorrow.
We’re early but moving fast:
- Stage: Seed funded
- Users: AI labs building audio or multi-modal language models
If you like working close to users, shipping quickly, and owning big pieces of product, you’ll probably enjoy this.
About the role
As our Founding Engineer, you’ll be one of the very first people on the team and a core partner to the founders. You’ll:
- Own large pieces of the product and stack end-to-end
- Help choose our architecture, tools, and engineering practices
- Ship quickly with real users, then iterate based on feedback
- Shape the culture of the engineering org we’ll build around you
This is not a narrow “tickets” role — it’s closer to being a co-founder without all the fundraising/admin overhead.
What you’ll do
In your first 6–12 months, you might:
- Design, build, and ship features across the stack — from data models and APIs to polished UI
- Work directly with founders and customers to understand problems and turn fuzzy requirements into simple, shipped solutions
- Set up the foundations of our infrastructure: CI/CD, observability, testing, security, and performance baselines
- Make key technical decisions (languages, frameworks, architecture) and document the reasoning so others can build on it
- Establish engineering best practices: code review norms, testing standards, release processes
- Help interview, hire, and onboard future engineers and contractors
- Roll up your sleeves on whatever is needed — product, support, docs, or debugging gnarly production issues
- Building data pipelines, evaluation toolings and improving our annotation platform.
You might be a good fit if
- You’ve shipped real products end-to-end at a startup or small, fast-moving team
- You’re comfortable working across the stack, including mobile development
- You like owning problems, not just tasks: you care about the user outcome, not just the code
- You’re happy working with ambiguous specs and iterating directly with founders and users
- You value clean, pragmatic engineering: tests, good abstractions, and simple designs over cleverness
- You communicate clearly, give and receive feedback well, and enjoy collaborating closely with non-engineers
Nice to have (but not required)
- Experience at an early-stage startup (seed/Series A or earlier)
- Exposure to AI / data / infra / devtools / audio (depending on your domain)
- Comfort with our current stack: [e.g. React, TypeScript, Node, Firebase/Firestore, GCP]
- Experience setting up analytics, experimentation, or data pipelines
- Prior work on developer tools, internal tools, or workflows where reliability really matters
Why you might be excited to join
- High ownership: You’ll influence product scope, technical direction, and engineering culture from day one.
- Massive impact: Your work will directly shape what Besimple becomes over the next few years.
- Mentorship & visibility: Work directly with the founders on product, fundraising, and strategy — not just code.
- Compensation: Competitive salary plus meaningful early equity
- Flexibility: We are in-person in Redwoodt City, CA with flexible hybrid options
- Benefits: Health, dental, vision, 401k, etc.
Why you might not be excited
- You prefer clear, stable requirements over messy, fast iteration
- You want a large team with established processes rather than helping create those processes
- You’re looking for a narrow specialist role instead of doing a bit of everything
- You’re not comfortable with the risk/ambiguity that comes with very early-stage startups
About Besimple AI
Why Us
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.
Why This Matters
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.
Traction & Customers
Who You’ll Work With
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.
How We Work
- 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.
