Research and improve real-time conversational AI for voice drive-thru ordering. Responsibilities include advancing speech, intent, turn-taking, voice activity detection, and order-accuracy models; curating and generating data for rare scenarios; building statistically rigorous benchmarks; evaluating audio and conversational quality under real-time constraints; and translating research into production improvements with applied AI and backend teams.
About the Role
At Arc, we are on a mission to amplify employees at local businesses using AI. With years of experience building and scaling commerce tools for businesses—from farmers markets and neighborhood coffee shops to global retailers, restaurant chains, and major sports venues—we know how to deliver mission-critical technology that integrates easily, delights customers, and works every time.
Voice drive-thru is our first product on that journey: an AI voice agent that takes real orders at the drive-thru speaker, in real time, integrating directly with a business's existing POS and hardware. The hard part isn't just "can an LLM take an order" — it's making the whole conversational loop (does the customer sound done talking, was the transcript actually right, did the model behave consistently across a thousand near-identical calls) hold up against real noise, real accents, real mumbled orders, and a live customer who won't repeat themselves.
As an AI Researcher, you'll push on the modeling and methodology underneath that loop: turn taking, voice activity detection, speech accuracy, order accuracy, detecting side conversations, and the rigorous experimentation needed to know — with statistical confidence, not vibes — whether a change actually made things better. This is a research role in service of a product that's live in real drive-thru lanes today, not a lab role disconnected from production.
What You'll Do
- Improve the models that let the agent understand natural, real-world speech, understand their intent and converse with them naturally while ringing their order in realtime
- Own speech accuracy as an ongoing research problem
- Data modeling to test and train the system against situations that are rare or hard to collect from live calls
- Build benchmarking practices that give the team confidence a change actually helped, rather than just seeming better
- Evaluate models and approaches for both conversation quality and the underlying audio pipeline, balancing accuracy against real-time constraints
- Work with the applied AI and backend teams to turn research findings into real product improvements
What We're Looking For
- Strong applied ML/research background (industry or research lab), with hands-on experience taking models from experiment to production, not just publication
- Experience with speech/audio ML
- Solid Python skills and comfort working with training ML models from scratch
- Experience designing rigorous evaluation methodology — statistically sound benchmarking, synthetic data generation, or dataset curation — for non-deterministic systems.
- Comfort operating with ambiguity — you can take a fuzzy quality problem ("the agent barges in too much") and turn it into a measurable research question
- Strong execution from research idea to real production deployment
Why Join Us
You'll have significant influence over both the product and the research culture at an early-stage company solving a genuinely hard, high-stakes problem: real-time AI that talks to real customers, in service of a mission to help local businesses run better. Voice drive-thru is just the beginning — your decisions will help shape what comes next.
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