ML Research Scientist - Voice AI at Deepgram

Deepgram; États-Unis; À distance; USD 170 000 - 240 000; Plein temps

Résumé du poste

Deepgram is seeking an ML Research Scientist - Voice AI in the United States. You will train, fine-tune, and evaluate deep learning models for speech AI, build production-ready systems, and design robust evaluation benchmarks.

The role emphasizes ownership, fast iteration, and delivering measurable improvements at scale. We value experts who can ship ML to production, work with Python and PyTorch, and contribute to expanding voice capabilities while maintaining high-quality benchmarks.

Qualifications

• Deep experience training, fine-tuning, and evaluating deep learning models.
• Experience in speech-to-text / voice agents is a bonus.
• Track record of shipping ML to production.

Responsabilités

• Find where our models fall short and set targets for improvement.
• Design evaluations to show model improvements and justify them.
• Curate and generate data and fine-tune models.
• Build working systems quickly and ship them to production.

Connaissances

Deep learning

Fine-tuning models

Model evaluation

Speech AI

Distributed training

Python

PyTorch

Production ML

Outils

Python

PyTorch

Description du poste

ML Research Scientist - Voice AI at Deepgram in Newark

Career Trajectory: This is a high-impact individual contributor research role at a well-funded AI infrastructure company (Deepgram serves 5,000+ enterprise clients). It sits at the intersection of applied ML research and production engineering - a rare combination that builds a highly transferable, senior-level skill set.

Skills You’ll Develop:

• Deep learning model training, fine-tuning, and evaluation at production scale
• Speech AI (ASR, TTS, voice agents) - one of the fastest-growing AI verticals
• Distributed training and low-latency inference systems
• Designing benchmarks and evaluation frameworks - a highly valued, niche skill

Market Compensation: No salary is listed, but ML Research roles at comparable AI startups (Series B/C stage) typically pay $170k-$240k base plus equity. Deepgram's customer list (Twilio, Cloudflare) suggests strong commercial traction and equity may be meaningful.

Culture Signals:

• Green flag: "We don't require a specific degree" - signals meritocracy over credentialism
• Green flag: Fast shipping cycles (weeks, not quarters) - good for portfolio building
• Watch: "AI-first by default" and high ownership expectations suggest a lean, intense team - better suited to self-starters than those who prefer structured mentorship

Work-Life Balance: The "frontier" and fast-ship culture implies high output expectations. This is likely a strong career accelerator but may not be a low-stress environment.

Company Overview

Deepgram 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 Deepgram in production, including Twilio, Cloudflare, Sierra, Decagon, Vapi, Cresta, Granola, and Jack in the Box.

The Deepgram 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, Deepgram makes high-quality voice experiences practical to build and run at scale. More than 650,000 developers have used Deepgram to process more than 200,000 years of audio. Every day, more of the world's conversations with AI run on our models.

Deepgram is a research company that productizes as fast as possible. Four Operating Principles shape how we think, decide, and work together:

Compress before you communicate. You think it through first, then lead with the bottom line.

Resist entropy. You question what already exists before adding more process or tools.

Solve the category. You fix recurring problems at the source, not one case at a time.

Work at the frontier. You go after problems no one has solved yet.

We look for people who already work this way, and these principles shape how we grow and evaluate our team.

AI-First by Default

We build AI that millions of people talk to, and we run the company the same way. AI is how we centralize work here, and is core to how we operate.

That means more than using the tools. We expect you to:

Build, not just use. Design your own workflows and agents, and use AI to remove whole categories of work instead of doing the same work faster.

Own the output. AI can speed up the work. It doesn't make anyone less accountable for it. You understand, verify, and stand behind everything you share, including what an agent produces for you.

Spend the time you get back at the frontier. The capacity AI creates goes toward problems no one has solved yet.

Keep pace. Models and tools change every month, and so will your work. You adopt what's new quickly and drop what no longer earns its place.

We judge AI by what it helps you deliver and whether you stand behind the result, not by how often you use it. In interviews, expect to walk us through a workflow you built and where you drew the line between AI and your own judgment.

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.
• 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, 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

Notice: We're aware of individuals impersonating Deepgram recruiters. All legitimate Deepgram recruiting communication comes from an @ deepgram.com email address. If you've received a message claiming to be Deepgram, please forward it to [e-mail].

Is this ML Research Scientist role fully remote? The job posting indicates a US-based position and does not require a specific office location, suggesting remote work within the United States. No physical office is mentioned as a requirement. Does Deepgram require a specific degree for this research role? No. Deepgram explicitly states they don't require a specific degree — they want to see hard problems you've solved through past projects, shipped systems, or open source work. What technical skills are needed for this position? Strong Python and PyTorch skills are required, along with deep experience training, fine-tuning, and evaluating deep learning models, plus a track record of shipping ML systems to production. Experience with speech AI or distributed training is a bonus. What is the salary range for this role at Deepgram? No salary range is disclosed in the job posting. Based on market data, ML Research Scientist roles at AI-focused startups of Deepgram's stage typically range from $170,000 to $240,000+ annually, potentially with equity. What kinds of projects will I work on as an ML Research Scientist at Deepgram? Example projects include expanding speech models to new languages, improving text-to-speech pronunciation, isolating voices from background noise, launching new voice capabilities like cloned voices, and releasing public benchmarks for voice AI.

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