Research Staff, LLMs [EMEA - Remote]

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.

Operating Principles:

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

Voice is the most natural modality for human interaction with machines. However, current sequence modeling paradigms based on jointly scaling model and data cannot deliver voice AI capable of universal human interaction. The challenges are rooted in fundamental data problems posed by audio: real-world audio data is scarce and enormously diverse, spanning a vast space of voices, speaking styles, and acoustic conditions. Even if billions of hours of audio were accessible, its inherent high dimensionality creates computational and storage costs that make training and deployment prohibitively expensive at world scale. We believe that entirely new paradigms for audio AI are needed to overcome these challenges and make voice interaction accessible to everyone.

The Role

Deepgram is currently looking for an experienced researcher to who has worked extensively with Large Language Models (LLMS) and has a deep understanding of transformer architecture to join our Research Staff. As a Member of the Research Staff, this individual should have extensive experience working on the hard technical aspects of LLMs, such as data curation, distributed large-scale training, optimization of transformer architecture, and Reinforcement Learning (RL) training.

The Challenge

We are seeking researchers who:

• See "unsolved" problems as opportunities to pioneer entirely new approaches

• Can identify the one critical experiment that will validate or kill an idea in days, not months

• Have the vision to scale successful proofs-of-concept 100x

• Are obsessed with using AI to automate and amplify your own impact

If you find yourself energized rather than daunted by these expectations—if you're already thinking about five ideas to try while reading this—you might be the researcher we need. This role demands obsession with the problems, creativity in approach, and relentless drive toward elegant, scalable solutions. The technical challenges are immense, but the potential impact is transformative.

What You'll Do

• Brainstorming and collaborating with other members of the Research Staff to define new LLM research initiatives

• Broad surveying of literature, evaluating, classifying, and distilling current methods

• Designing and carrying out experimental programs for LLMs

• Driving transformer (LLM) training jobs successfully on distributed compute infrastructure and deploying new models into production

• Documenting and presenting results and complex technical concepts clearly for a target audience

• Staying up to date with the latest advances in deep learning and LLMs, with a particular eye towards their implications and applications within our products

You'll Love This Role if You

• Are passionate about AI and excited about working on state of the art LLM research

• Have an interest in producing and applying new science to help us develop and deploy large language models

• Enjoy building from the ground up and love to create new systems.

• Have strong communication skills and are able to translate complex concepts clearly

• Are highly analytical and enjoy delving into detailed analyses when necessary

It's Important to Us That You Have

• 3+ years of experience in applied deep learning research, with a solid understanding toward the applications and implications of different neural network types, architectures, and loss mechanism

• Proven experience working with large language models (LLMs) - including experience with data curation, distributed large-scale training, optimization of transformer architecture, and RL Learning

• Strong experience coding in Python and working with Pytorch

• Experience with various transformer architectures (auto-regressive, sequence-to-sequence.etc)

• Experience with distributed computing and large-scale data processing

• Prior experience in conducting experimental programs and using results to optimize models

It Would Be Great if You Had

• Deep understanding of transformers, causal LMs, and their underlying architecture

• Understanding of distributed training and distributed inference schemes for LLMs

• Familiarity with RLHF labeling and training pipelines

• Up-to-date knowledge of recent LLM techniques and developments

The Challenge

We are seeking researchers who:

• See "unsolved" problems as opportunities to pioneer entirely new approaches

• Can identify the one critical experiment that will validate or kill an idea in days, not months

• Have the vision to scale successful proofs-of-concept 100x

• Are obsessed with using AI to automate and amplify your own impact

If you find yourself energized rather than daunted by these expectations—if you're already thinking about five ideas to try while reading this—you might be the researcher we need. This role demands obsession with the problems, creativity in approach, and relentless drive toward elegant, scalable solutions. The technical challenges are immense, but the potential impact is transformative.

What You'll Do

• Brainstorming and collaborating with other members of the Research Staff to define new LLM research initiatives

• Broad surveying of literature, evaluating, classifying, and distilling current methods

• Designing and carrying out experimental programs for LLMs

…