Research Staff, Voice AI Foundations

Company Overview

Deepgram is the leading voice AI platform for developers building speechtotext (STT), texttospeech (TTS) and full speechtospeech (STS) offerings. 200,000+ developers build with Deepgram’s voicenative foundational models – accessed through APIs or as selfmanaged software – due to our unmatched accuracy, latency and pricing. Customers include software companies building voice products, cosell partners working with large enterprises, and enterprises solving internal voice AI use cases. The company ended 2024 cashflow positive with 400+ enterprise customers, 3.3x annual usage growth across the past 4 years, over 50,000 years of audio processed and over 1 trillion words transcribed. There is no organization in the world that understands voice better than Deepgram

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: realworld 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

As a Member of the Research Staff, you will pioneer the development of Latent Space Models (LSMs), a new approach that aims to solve the fundamental data, scale, and cost challenges associated with building robust, contextualized voice AI. Your research will focus on solving one or more of the following problems:

• Build nextgeneration neural audio codecs that achieve extreme, low bitrate compression and high fidelity reconstruction across a worldscale corpus of general audio.

• Pioneer steerable generative models that can synthesize the full diversity of human speech from the codec latent representation, from casual conversation to highly emotional expression to complex multispeaker scenarios with environmental noise and overlapping speech.

• Develop embedding systems that cleanly factorize the codec latent space into interpretable dimensions of speaker, content, style, environment, and channel effects enabling precise control over each aspect and the ability to massively amplify an existing seed dataset through “latent recombination”.

• Leverage latent recombination to generate synthetic audio data at previously impossible scales, unlocking joint model and data scaling paradigms for audio. Endeavor to train multimodal speechtospeech systems that can 1) understand any human irrespective of their demographics, state, or environment and 2) produce empathic, humanlike responses that achieve conversational or taskoriented objectives.

• Design model architectures, training schemes, and inference algorithms that are adapted for hardware at the bare metal enabling cost efficient training on billionhour datasets and powering realtime inference for hundreds of millions of concurrent conversations.

• 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 proofsofconcept 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 youre 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.

    
    Its Important to Us That You Have

    • Strong mathematical foundation in statistical learning theory, particularly in areas relevant to selfsupervised and multimodal learning

    • Deep expertise in foundation model architectures, with an understanding of how to scale training across multiple modalities

    • Proven ability to bridge theory and practice—someone who can both derive novel mathematical formulations and implement them efficiently

    • Demonstrated ability to build data pipelines that can process and curate massive datasets while maintaining quality and diversity

    • Track record of designing controlled experiments that isolate the impact of architectural innovations and validate theoretical insights

    • Experience optimizing models for realworld deployment, including knowledge of hardware constraints and efficiency techniques

    • History of opensource contributions or research publications that have advanced the state of the art in speechlanguage AI

    • 
      How We Generated This Job Description

      This job description was generated in two parts. The “Opportunity”, “Role”, and “Challenge” sections were generated by a human using Claude3.5sonnet as a writing partner. The objective of these sections is to clearly state the problem that Deepgram is attempting to solve, how we intend to solve it, and some guidelines to help you decide if Deepgram is right for you. Therefore, it is important that this section was articulated by a human.

      The “It’s Important to Us” section was automatically derived from a multistage LLM analysis (using o1) of key foundational deep learning papers related to our research goals. This work was completed as an experiment to test the hypothesis that traits of highly productive and impactful researchers are reflected directly in their work. The analysis focused on understanding how successful researchers approach problems, from mathematical foundations through to practical deployment. The problems Deepgram aims to solve are immensely difficult and span multiple disciplines and specialties. As such, we chose seminal papers that we believe reflect the pioneering work and exemplary human characteristics needed for success. The LLM analysis culminates in an “Ideal Researcher Profile”, which is reproduced below along with the list of foundational papers.

      
      
      Ideal Researcher Profile

      An ideal researcher, as evidenced by the recurring themes across these foundational papers, excels in five key areas: (1) Statistical & Mathematical Foundations, (2) Algorithmic Innovation & Implementation, (3) DataDriven & Scalable Systems, (4) Hardware & Systems Understanding, and (5) Rigorous Experimental Design. Below is a synthesis of how each paper highlights these qualities, with references illustrating why they matter for building robust, impactful deep learning models.

      
      
      1. Statistical & Mathematical Foundations

      Mastery of Core Concepts

      Many papers, like Scaling Laws for Neural Language Models and Neural Discrete Representation Learning (VQVAE), reflect the importance of powerlaw analyses, derivation of novel losses, or adaptation of fundamental equations (e.g., in VQVAEs commitment loss or rectified flows in Scaling Rectified Flow Transformers). Such mathematical grounding clarifies why models converge or suffer collapse.

      Combining Existing Theories in Novel Ways

      Papers such as Moshi (combining text modeling, audio codecs, and hierarchical generative modeling) and Finite Scalar Quantization (FSQs adaptation of classic scalar quantization to replace vectorquantized representations) show how reusing but reimagining known techniques can yield breakthroughs. Many references (e.g., the structured statespace duality in Transformers are SSMs) underscore how unifying previously separate research lines can reveal powerful algorithmic or theoretical insights.

      Logical Reasoning and Assumption Testing

      Across all papers—particularly in the problem statements of Whisper or Rectified Flow Transformers—the authors present assumptions (e.g., scaling data leads to zeroshot robustness or straightline noise injection improves sample efficiency) and systematically verify them with thorough empirical results. An ideal researcher similarly grounds new ideas in wellformed, testable hypotheses.

      
      2. Algorithmic Innovation & Implementation

      Creative Solutions to Known Bottlenecks

      Each paper puts forth a unique algorithmic contribution—Rectified Flow Transformers redefines standard diffusion paths, FSQ proposes simpler scalar quantizations contrasted with VQ, phi3 mini relies on curated data and blocksparse attention, and Mamba2 merges SSM speed with attention concepts.

      Turning Theory into Practice

      Whether its the direct preference optimization (DPO) for alignment in phi3 or the residual vector quantization in SoundStream, these works show that bridging design insights with implementable prototypes is essential.

      Clear Impact Through Prototypes & OpenSource

      Many references (Whisper, neural discrete representation learning, Mamba2) highlight releasing code or pretrained models, enabling the broader community to replicate and build upon new methods. This premise of collaboration fosters faster progress.

      
      3. DataDriven & Scalable Systems

      Emphasis on LargeScale Data and Efficient Pipelines

      Papers such as Robust Speech Recognition via LargeScale Weak Supervision (Whisper) and BASE TTS demonstrate that collecting and processing hundreds of thousands of hours of realworld audio can unlock new capabilities in zeroshot or lowresource domains. Meanwhile, phi3 Technical Report shows that filtering and curating data at scale (e.g., data optimal regime) can yield high performance even in smaller models.

      Strategic Use of Data for Staged Training

      A recurring strategy is to vary sources of data or the order of tasks. Whisper trains on multilingual tasks, BASE TTS uses subsetsstages for pretraining on speech tokens, and phi3 deploys multiple training phases (web data, then synthetic data). This systematic approach to data underscores how an ideal researcher designs training curricula and data filtering protocols for maximum performance.

      
      4. Hardware & Systems Understanding

      Efficient Implementations at Scale

      Many works illustrate how researchers tune architectures for modern accelerators: the InDatacenter TPU paper exemplifies domainspecific hardware design for dense matrix multiplications, while phi3 leverages blocksparse attention and custom Triton kernels to run advanced LLMs on resourcelimited devices.

      RealTime & OnDevice Constraints

      SoundStream shows how to compress audio in real time on a smartphone CPU, demonstrating that knowledge of hardware constraints (latency, limited memory) drives design choices. Similarly, Moshis lowlatency streaming TTS and phi3minis phonebased inference highlight that an ideal researcher must adapt algorithms to resource limits while maintaining robustness.

      Architectural & Optimization Details

      Papers like Mamba2 in Transformers are SSMs and the InDatacenter TPU work show how exploiting specialized matrix decomposition, custom memory hierarchies, or quantization approaches can lead to breakthroughs in speed or energy efficiency.

      
      5. Rigorous Experimental Design

      Controlled Comparisons & Ablations

      Nearly all papers—Whisper, FSQ, Mamba2, BASE TTS—use systematic ablations to isolate the impact of individual components (e.g., ablation on vectorquantization vs. scalar quantization in FSQ, or size of codebooks in VQVAEs). This approach reveals which design decisions truly matter.

      Multifold Evaluation Metrics

      From MUSHRA listening tests (SoundStream, BASE TTS) to FID in image synthesis (Scaling Rectified Flow Transformers, FSQ) to perplexity or zeroshot generalization in language (phi3, Scaling Laws for Neural Language Models), the works demonstrate the value of comprehensive, carefully chosen metrics.

      Stress Tests & Edge Cases

      Whispers outofdistribution speech benchmarks, SoundStreams evaluation on speech + music, or Mamba2s performance on multiquery associative recall demonstrate the importance of specialized challenge sets. Researchers who craft or adopt rigorous benchmarks and redteam their models (as in phi3 safety alignment) are better prepared to address realworld complexities.

      
      
      Summary

      Overall, an ideal researcher in deep learning consistently demonstrates:

      • A solid grounding in theoretical and statistical principles

      • A talent for proposing and validating new algorithmic solutions

      • The capacity to orchestrate data pipelines that scale and reflect realworld diversity

      • Awareness of hardware constraints and systemlevel tradeoffs for efficiency

      • Thorough and transparent experimental practices

      • These qualities surface across research on speech (Whisper, BASE TTS), language modeling (Scaling Laws, phi3), specialized hardware (TPU, Transformers are SSMs), and new representation methods (VQVAE, FSQ, SoundStream). By balancing these attributes—rigorous math, innovative algorithms, largescale data engineering, hardwaresavvy optimizations, and reproducible experimentation—researchers can produce impactful, trustworthy advancements in foundational deep learning.

        
        
        
        Foundational Papers

        This job description was generated through analysis of the following papers:

        • Robust Speech Recognition via LargeScale Weak Supervision (arXiv:2212.04356)

        • Moshi: a speechtext foundation model for realtime dialogue (arXiv:2410.00037)

        • Scaling Rectified Flow Transformers for HighResolution Image Synthesis (arXiv:2403.03206)

        • Scaling Laws for Neural Language Models (arXiv:2001.08361)

        • BASE TTS: Lessons from building a billionparameter TexttoSpeech model on 100K hours of data (arXiv:2402.08093)

        • InDatacenter Performance Analysis of a Tensor Processing Unit (arXiv:1704.04760)

        • Neural Discrete Representation Learning (arXiv:1711.00937)

        • SoundStream: An EndtoEnd Neural Audio Codec (arXiv:2107.03312)

        • Finite Scalar Quantization: VQVAE Made Simple (arXiv:2309.15505)

        • Phi3 Technical Report: A Highly Capable Language Model Locally on Your Phone (arXiv:2404.14219)

        • Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality (arXiv:2405.21060)

        • Backed by prominent investors including Y Combinator, Madrona, Tiger Global, Wing VC and NVIDIA, Deepgram has raised over $85 million in total funding. If youre looking to work on cuttingedge technology and make a significant impact in the AI industry, wed love to hear from you!

          Deepgram is an equal opportunity employer. We want all voices and perspectives represented in our workforce. We are a curious bunch focused on collaboration and doing the right thing. We put our customers first, grow together and move quickly. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, gender identity or expression, age, marital status, veteran status, disability status, pregnancy, parental status, genetic information, political affiliation, or any other status protected by the laws or regulations in the locations where we operate.

          We are happy to provide accommodations for applicants who need them.