Besimple AI
Voice data for AI
Mobile Engineer - Full time On-site (Internships available too)
About the role
About Besimple AI
Besimple AI is building the audio data and evaluation infrastructure for real-world voice AI deployment. We help AI teams collect, evaluate, and improve voice models across languages, accents, environments, and complex human interactions.
The founders are ex-Meta product and engineering leaders from MIT and Brown. We are a small, high-ownership team working directly with frontier AI labs and fast-moving AI companies to push the state of the art in audio models, voice agents, and human-in-the-loop evaluation.
About the role
We are looking for a full-time Mobile Engineer lead to own the mobile experience for our contributor data collection platforms.
This is a high-ownership role at the intersection of mobile engineering, audio recording, user experience, product, and AI data quality. You will own the mobile experiences that allow people around the world to record, review, submit, and improve audio data used to train and evaluate frontier voice AI systems.
This is not a generic app development role. You will work on real production workflows where mobile UX directly affects audio quality, contributor throughput, customer delivery, and model performance.
What you’ll do
- Build and maintain a mobile platform that will be used by tens of thousands of users for onboarding, audio recording, and live conversations.
- Own mobile features from product requirements through implementation, release, monitoring, and iteration.
- Work closely with founders, product, design, backend, and operations to turn messy real-world workflows into simple mobile experiences.
- Improve audio capture quality, upload reliability, offline behavior, latency, permissions, device compatibility, and error recovery.
- Build contributor-facing flows for recording tasks, auditions, metadata collection, QA feedback, payments, and project instructions.
- Instrument mobile workflows so we can understand conversion, task completion, audio quality, rework, and drop-off.
- Partner with backend engineering to design APIs, data models, and upload pipelines for mobile-first audio collection.
- Debug production issues across devices, OS versions, network conditions, and audio hardware.
- Help define the mobile architecture, release process, testing strategy, and engineering standards as the product scales.
What we’re looking for
- Strong experience building and shipping production mobile apps.
- Experience with React Native, Swift/iOS, Kotlin/Android.
- Strong product sense and care for clean, fast, intuitive user experiences.
- Comfort owning features end to end, from ambiguous problem to shipped product.
- Experience working with background uploads, low bandwidth network, crash reporting/analytics pipelines, push notifications, and app store releases.
- Strong debugging ability across frontend, backend, device, network, and user-behavior issues.
- Ability to write clean, maintainable code and make pragmatic architecture decisions.
- Comfortable working in ambiguity and building from scratch in a fast-moving startup.
- High ownership, low ego, and willingness to get hands-on with details that affect real users.
Strong pluses
- Opinionated about approaches in React Native, Expo, audio kits, streaming, WebRTC, speech, voice agents, ASR, TTS, or media-heavy mobile apps.
- Experience building contributor, marketplace, creator, gig-work, education, language-learning, or field-data-collection apps.
- Experience with offline-first workflows, resumable uploads, large file handling, or poor-network environments.
- Experience with mobile QA automation, device testing, performance profiling, or crash-rate reduction.
- Experience building internal tools or operational workflows, not just consumer-facing app surfaces.
- Experience in an early-stage startup or small, high-ownership engineering team.
Example projects you might own
- Building the mobile first experience for a new audio collection project in a priority language.
- Improving upload reliability for long audio files on unstable mobile networks.
- Designing a contributor onboarding flow that tests microphone quality, accent fit, task comprehension, and recording environment.
- Building AI native mobile QA feedback loops so contributors can understand and fix rejected submissions.
- Creating offline/resumable task flows for contributors in low-connectivity regions.
- Instrumenting the mobile funnel from invite to recording completion to accepted audio hour.
- Improving the mobile experience for voice actor auditions, scripted speech, roleplay conversations, or natural dialogue collection.
- Building internal debugging tools for audio submissions, device metadata, permissions failures, and upload failures.
What success looks like
You will be successful if Besimple can collect high-quality audio data through mobile workflows that are:
- Easy for contributors to complete
- Reliable across devices and network conditions
- High enough quality for model training and evaluation
- Measurable through clear product and quality metrics
- Fast to adapt for new customer projects
- Improving over time in conversion, quality, throughput, and automation
Your core metrics may include:
- Contributor activation rate
- Task completion rate
- Accepted audio hours submitted through mobile
- Upload success rate
- Rejection / rework rate
- Crash-free sessions
- Recording quality pass rate
- Time to complete task
- Mobile funnel conversion
- Number of reusable mobile workflows shipped
Why join us
This is a high-impact role at an early-stage AI company. You will not just build screens; you will help define how real-world audio data gets collected, evaluated, and improved for the next generation of voice AI.
You will work directly with the founders and users, own product-critical mobile workflows, and shape the engineering foundation for a platform used by AI teams building real production voice systems.
Source used for structure and company context: original YC job posting.
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.
