Research Staff, Applied Voice AI

COMPANY OVERVIEW

The company is bringing the world’s most natural interface, voice, to the technology we use every day. Our speech models and real-time infrastructure power AI systems that keep up with the way people actually speak. More than 5,000 organizations run conversational voice AI on the company in production, including Twilio, Cloudflare, Sierra, Decagon, Vapi, Cresta, Granola, and Jack in the Box.

The company Voice Agent Platform brings together speech-to-text, text-to-speech and speech-to-speech, with voice-native foundation models available through cloud APIs or self-hosted and on-premises deployments. Built for accuracy, low latency and cost efficiency, the company makes high-quality voice experiences practical to build and run at scale. More than 650,000 developers have used the company to process more than 200,000 years of audio. Every day, more of the world’s conversations with AI run on our models.

AI-FIRST BY DEFAULT

At the company, we expect an AI-first mindset—AI use and comfort aren’t optional, they’re core to how we operate, innovate, and measure performance.

Every team member who works at the company is expected to actively use and experiment with advanced AI tools, and even build your own into your everyday work. We measure how effectively AI is applied to deliver results, and consistent, creative use of the latest AI capabilities is key to success here. Candidates should be comfortable adopting new models and modes quickly, integrating AI into their workflows, and continuously pushing the boundaries of what these technologies can do.

Additionally, we move at the pace of AI. Change is rapid, and you can expect your day-to-day work to evolve just as quickly. If you’re energized by experimenting, adapting, and learning constantly, you’ll thrive here. Expect your day-to-day to evolve as fast as the technology does.

THE OPPORTUNITY

We're building the engine that solves customer challenges at scale. When customers hit a limit in our voice AI, you'll measure it, fix it, and ship the fix to every customer in weeks.

Example projects

• Expanding our models to new languages and regions

• Improving how text-to-speech pronounces names, terms, and numbers

• Isolating a speaker's voice from background noise and crosstalk

• Giving customers early access to new voice capabilities, like steerable and cloned voices, and using their feedback to guide the research methodology

• Releasing new benchmarks for Voice AI use-cases

THE ROLE

You will thrive in this role if you enjoy

• Turning vague customer problems into clear metrics

• Using whatever works to fix them, including fine-tuning, new data, and external models

• Shipping work that customers use within weeks

THE CHALLENGE

We are seeking researchers who

• Own a customer problem until it measurably improves in production

• Find the fastest path to a fix, even if it's an approach no one has tried

• Can tell when a result is ready for customers

If these expectations energize you, you might be the person we need. This role takes focus on the customer's problem, creativity in approach, and a strong drive to ship.

WHAT YOU'LL DO

• Find where our models fall short and set targets for improvement

• Design evaluations that show whether a model is better and why

• Curate and generate data and fine-tune models

• Build working systems quickly and ship them to production

We don't require a specific degree. Show us hard problems you've solved in past projects, shipped systems, or open source work.

YOU MAY BE A GOOD FIT IF YOU HAVE

• Deep experience training, fine-tuning, and evaluating deep learning models

• Strong Python and PyTorch skills

• A track record of shipping ML to production

• A habit of owning problems end to end and learning what you need along the way

• Good judgment about which problems matter and when a result is ready to ship

STRONG CANDIDATES MAY ALSO HAVE

• Experience with speech-to-text, text-to-speech, or voice agents

• Experience with distributed training

• Published papers, open source contributions, or public benchmarks

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