Research Engineer, Post-Training

PromptCube; San Francisco (CA); Remote; USD 178,000 - 268,000; Full time

Job summary

Deepgram is seeking a Research Engineer, Post-Training to turn vague customer problems into measurable model improvements. You will design evaluations, curate data, fine-tune models, and ship production fixes to customers within weeks.

You will own problems end to end, expand models to new languages, and improve TTS pronounciation and speaker separation while enabling early access to advanced voice capabilities. This role emphasizes rapid experimentation in a fast-changing AI environment.

Qualifications

• Deep experience training, fine-tuning, and evaluating deep learning models.
• Strong Python and PyTorch skills.
• A track record of shipping machine learning to production.
• A habit of owning problems end to end and learning what is needed along the way.
• Good judgment about which problems matter and when a result is ready to ship.
• No specific degree is required; candidates should demonstrate hard problems solved in past projects, shipped systems, or open-source work.
• Active use and experimentation with advanced AI tools, including integrating AI into workflows and building tools into everyday work; AI application is measured as part of performance.
• Comfort adapting quickly to new AI models and modes of working in a fast-changing environment.

Responsibilities

• Identify where Deepgram's voice models fall short and set targets for measurable improvement.
• Design evaluations that show whether a model is better and explain why.
• Curate and generate training data, then fine-tune models to address customer-facing gaps.
• Build working systems quickly and ship them to production, owning a customer problem until it measurably improves.
• Expand models to new languages and regions.
• Improve text-to-speech pronunciation of names, terms, and numbers, and isolate a speaker's voice from background noise and crosstalk.
• Give customers early access to new voice capabilities such as steerable and cloned voices, use their feedback to guide research methodology, and release new benchmarks for Voice AI use cases.

Skills

Deep learning

Python

PyTorch

Production ML

End-to-end ownership

Job description

Role overview

Deepgram, a voice AI platform providing real-time speech-to-text, text-to-speech, and voice-agent APIs, is hiring a Research Engineer, Post-Training to turn vague customer problems into measurable model improvements. The role spans evaluation design, data curation, fine-tuning, and shipping production fixes to customers within weeks.

Responsibilities

• Identify where Deepgram's voice models fall short and set targets for measurable improvement.
• Design evaluations that show whether a model is better and explain why.
• Curate and generate training data, then fine-tune models to address customer-facing gaps.
• Build working systems quickly and ship them to production, owning a customer problem until it measurably improves.
• Expand models to new languages and regions.
• Improve text-to-speech pronunciation of names, terms, and numbers, and isolate a speaker's voice from background noise and crosstalk.
• Give customers early access to new voice capabilities such as steerable and cloned voices, use their feedback to guide research methodology, and release new benchmarks for Voice AI use cases.

Qualifications

• Deep experience training, fine-tuning, and evaluating deep learning models.
• Strong Python and PyTorch skills.
• A track record of shipping machine learning to production.
• A habit of owning problems end to end and learning what is needed along the way.
• Good judgment about which problems matter and when a result is ready to ship.
• No specific degree is required; candidates should demonstrate hard problems solved in past projects, shipped systems, or open-source work.
• Active use and experimentation with advanced AI tools, including integrating AI into workflows and building tools into everyday work; AI application is measured as part of performance.
• Comfort adapting quickly to new AI models and modes of working in a fast-changing environment.

Application conditions

• Full-time position.
• Listed locations: USA
• Remote; San Francisco, CA; New York City, NY.
• Remote work does not imply worldwide eligibility. Residency, work authorization, time-zone, language, and travel requirements are not specified in the source and must be checked in the original listing.

Preferred qualifications

• Experience with speech-to-text, text-to-speech, or voice agents.
• Experience with distributed training.
• Published papers, open-source contributions, or public benchmarks.

Compensation and benefits

• Annual salary range: $178,000-$268,000 USD.

Employer-published remote role. Listed location(s): USA

• Remote; San Francisco, CA; New York City, NY. Remote does not imply worldwide eligibility. Check the original listing for residency, work authorization, time zone, language and travel requirements.

Company Overview

Deepgram is the leading platform underpinning the emerging trillion-dollar Voice AI economy, providing real-time APIs for speech-to-text (STT), text-to-speech (TTS), and building production-grade voice agents at scale. More than 200,000 developers and 1,300+ organizations build voice offerings that are ‘Powered by Deepgram’, including Twilio, Cloudflare, Sierra, Decagon, Vapi, Daily, Cresta, Granola, and Jack in the Box. Deepgram’s voice-native foundation models are accessed through cloud APIs or as self-hosted and on-premises software, with unmatched accuracy, low latency, and cost efficiency.

Backed by a recent Series C led by leading global investors and strategic partners, Deepgram has processed over 50,000 years of audio and transcribed more than 1 trillion words. There is no organization in the world that understands voice better than Deepgram.

Company Operating Rhythm

At Deepgram, 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 Deepgram 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. This may not be the right role if you’re not excited to experiment, adapt, think on your feet, and learn constantly, or if you’re seeking something highly prescriptive with a traditional 9-to-5.

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

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].