Audio QA Lead - Part Time Contractor

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.

We are hiring an Audio QA Lead to support the development of high-quality training datasets for next-generation voice AI models.

In this role, you will work hands-on to improve the quality, consistency, and usability of speech datasets across applications such as text-to-speech, transcription, speech-to-speech, ASR, and conversational voice systems. Your work will directly influence how data is collected, reviewed, and delivered for real-world model training.

You will work across three core areas: defining and applying audio quality standards, recording high-quality speech on demand, and performing annotation and QA across speech datasets. This is not a generic audio production role. The work focuses on making audio usable for model training and requires a strong understanding of how data quality impacts model.

This is a part-time contractor role that can turn into full-time role.

• Develop, refine, and apply audio quality guidelines for speech and voice datasets.
• Review audio files against technical, linguistic, and task-specific standards, making clear approval, rejection, or revision decisions.
• Identify audio and annotation issues such as background noise, clipping, distortion, plosives, echo, low signal, segmentation errors, transcript mismatches, and speaker-label inconsistencies.
• Perform annotation and QA tasks, including transcription, timestamp validation, VAD/segmentation, diarization, pronunciation checks, and metadata review.
• Record speech based on provided scripts and performance guidelines, delivering natural, high-quality, specification-compliant audio.
• Document edge cases, update review rubrics, and improve internal SOPs and quality standards.
• Collaborate with research, ML, and operations teams to translate model requirements into data specifications and evaluation criteria.
• Ensure consistency and integrity across audio files, transcripts, annotations, and associated metadata.

The ideal candidate has direct experience working with audio AI datasets and understands what makes speech data effective for model training. You have a strong ear for audio quality, are comfortable applying annotation standards, and can consistently produce and evaluate high-quality recordings.

• Direct experience working with audio AI training datasets or evaluation workflows.
• Hands-on experience with TTS, ASR, transcription, speech-to-speech, or related voice AI systems.
• Experience developing or applying audio quality standards in production environments.
• Experience with speech annotation tasks such as transcription, timestamp QA, VAD/segmentation, and diarization.
• Strong auditory judgment with the ability to consistently identify subtle audio quality issues.
• Ability to produce high-quality recordings in a controlled, quiet environment using professional or near-professional equipment.
• Strong written communication skills with the ability to provide clear, actionable feedback.
• High attention to detail and sound judgment when evaluating edge cases.
• Comfort working with structured data formats such as spreadsheets, CSV, or JSON.

• Experience with audio tools such as Audacity, Praat, or similar.
• Basic scripting skills in Python, Bash, or SQL for QA or dataset analysis.
• Background in linguistics, phonetics, speech research, or voiceover work.
• Experience evaluating both real and synthetic audio.
• Multilingual experience or familiarity with accents and dialect variation.
• Familiarity with compliant handling of consented and licensed voice data.

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.