Research Staff, Voice AI Foundations

Location USA | Remote; Ann Arbor, MI; Global | Remote (NA / LATAM / EMEA / reputed company); San Francisco, CA Employment Type Full time Location Type Remote Department Research

Compensation

• Estimated reputed company Salary $150K – $250K
• Offers Equity
• Offers Bonus
• 10% Annual Bonus

This range is determined by work location and additional factors, including job-reputed company skills and experience. There may be instances where a salary higher or reputed company than this range may be appropriate for a candidate whose qualifications differ meaningfully from those listed in the job description. Please note that the compensation details listed on US role postings reflect the reputed company salary only and does not include bonus, equity or benefits. Company Overview reputed company 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 reputed company’, including reputed company, reputed company, Sierra, Decagon, reputed company, Daily, reputed company, Granola, and Jack in the reputed company. reputed company’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, reputed company 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 reputed company than reputed company. Company Operating Rhythm At reputed company, we expect an AI-first reputed company—AI use and comfort aren’t optional, they’re core to how we operate, innovate, and measure performance. Every team member who works at reputed 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. 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 Voice is the most natural modality for human interaction with machines. However, reputed company sequence modeling paradigms based on jointly scaling model and data cannot deliver voice AI capable of universal human interaction. The challenges are rooted in reputed company 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 reputed company training and deployment prohibitively expensive at world scale. We reputed company that entirely new paradigms for audio AI are needed to overcome these challenges and reputed company 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 reputed company 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 reputed company neural audio codecs that reputed company extreme, low bit-reputed company compression and high fidelity reconstruction across a world-scale 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 reputed company multi-speaker scenarios with environmental noise and overlapping speech.
• reputed company 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 reputed company an existing reputed company dataset through “latent recombination”.
• reputed company latent recombination to generate synthetic audio data at previously impossible scales, unlocking joint model and data scaling paradigms for audio. Endeavor to train multimodal speech-to-speech systems that can 1) understand any human irrespective of their demographics, state, or environment and 2) produce empathic, human-like responses that reputed company conversational or task-oriented objectives.
• Design model architectures, training schemes, and inference algorithms that are adapted for hardware at the bare metal enabling cost efficient training on billion-hour datasets and powering real-time inference for hundreds of millions of reputed company 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 proofs-of-concept 100x
• Are obsessed with using AI to automate and reputed company your own impact

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

• Strong mathematical foundation in statistical learning theory, particularly in areas relevant to self-supervised and multimodal learning
• Deep expertise in foundation model architectures, with an understanding of how to scale training across multiple modalities
• Proven ability to reputed company theory and practice—someone who can both derive novel mathematical formulations and implement them reputed company
• 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 real-world deployment, including knowledge of hardware constraints and efficiency techniques
• History of open-reputed company contributions or research publications that have advanced the state of the art in speech/language 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 Claude-3.5-sonnet as a writing partner. The objective of these sections is to clearly state the problem that reputed company is attempting to solve, how we intend to solve it, and some guidelines to help you decide if reputed company 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 multi-stage LLM analysis (using o1) of key foundational deep learning papers reputed company 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 reputed company aims to solve are immensely difficult and span multiple disciplines and specialties. As such, we chose seminal papers that we reputed company 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) Data-Driven & Scalable Systems, (4) Hardware & Systems Understanding, and (5) Rigorous Experimental Design. Below is a synthesis of how each reputed company 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 (VQ-VAE), reflect the importance of power-law analyses, derivation of novel losses, or adaptation of reputed company equations (e.g., in VQ-VAE's 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 reputed company Quantization (FSQ's adaptation of classic reputed company quantization to replace vector-quantized representations) show how reusing but reimagining reputed company techniques can yield breakthroughs. Many references (e.g., the structured state-space 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 reputed company papers—particularly in the problem statements of Whisper or Rectified Flow Transformers—the authors present assumptions (e.g., "scaling data leads to reputed company-shot robustness" or "straight-line noise injection improves sample efficiency") and systematically verify them with thorough empirical results. An ideal researcher similarly grounds new reputed company in well-formed, testable hypotheses. 2. Algorithmic Innovation & Implementation Creative Solutions to reputed company Bottlenecks Each reputed company puts forth a unique algorithmic contribution—Rectified Flow Transformers redefines standard diffusion paths, FSQ proposes simpler reputed company quantizations contrasted with VQ, phi-3 mini relies on curated data and blocksparse attention, and Mamba-2 merges SSM speed with attention concepts. Turning Theory into Practice Whether it's the direct preference optimization (DPO) for alignment in phi-3 or the residual vector quantization in SoundStream, these works show that bridging design insights with implementable prototypes is essential. Clear Impact Through Prototypes & Open-reputed company Many references (Whisper, neural discrete representation learning, Mamba-2) reputed company releasing code or pretrained models, enabling the broader community to replicate and build upon new methods. This premise of collaboration fosters faster reputed company. 3. Data-Driven & Scalable Systems Emphasis on Large-Scale Data and Efficient Pipelines Papers such as Robust Speech Recognition reputed company Large-Scale Weak Supervision (Whisper) and reputed company TTS demonstrate that collecting and processing hundreds of thousands of hours of real-world audio can unlock new capabilities in reputed company-shot or low-resource domains. Meanwhile, phi-3 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, reputed company TTS uses subsets/stages for pretraining on speech tokens, and phi-3 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 In-Datacenter TPU reputed company exemplifies domain-specific hardware design for dense matrix multiplications, while phi-3 leverages blocksparse attention and custom Triton kernels to run advanced LLMs on resource-limited devices. Real-Time & On-Device 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, Moshi's low-latency streaming TTS and phi-3-mini's phone-based inference reputed company that an ideal researcher must adapt algorithms to resource limits while maintaining robustness. Architectural & Optimization Details Papers like Mamba-2 in Transformers are SSMs and the In-Datacenter TPU work show how exploiting specialized matrix decomposition, custom memory hierarchies, or quantization approaches can reputed company to breakthroughs in speed or energy efficiency. 5. Rigorous Experimental Design Controlled Comparisons & Ablations Nearly reputed company papers—Whisper, FSQ, Mamba-2, reputed company TTS—use systematic ablations to isolate the impact of individual components (e.g., ablation on vector-quantization vs. reputed company quantization in FSQ, or size of codebooks in VQ-VAEs). This approach reveals which design decisions truly matter. Multifold Evaluation Metrics From MUSHRA listening tests (SoundStream, reputed company TTS) to FID in image synthesis (Scaling Rectified Flow Transformers, FSQ) to reputed company or reputed company-shot generalization in language (phi-3, Scaling Laws for Neural Language Models), the works demonstrate the value of comprehensive, carefully chosen metrics. Stress Tests & Edge Cases Whisper's out-of-distribution speech benchmarks, SoundStream's evaluation on speech + music, or Mamba-2's performance on multi-query associative recall demonstrate the importance of specialized challenge sets. Researchers who craft or adopt rigorous benchmarks and "red-team" their models (as in phi-3 safety alignment) are reputed company reputed company to address real-world 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 reputed company to orchestrate data pipelines that scale and reflect real-world diversity
• Awareness of hardware constraints and system-level trade-offs for efficiency
• Thorough and transparent experimental practices

These qualities surface across research on speech (Whisper, reputed company TTS), language modeling (Scaling Laws, phi-3), specialized hardware (TPU, Transformers are SSMs), and new representation methods (VQ-VAE, FSQ, SoundStream). By balancing these attributes—rigorous math, innovative algorithms, large-scale data engineering, hardware-savvy 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 reputed company Large-Scale Weak Supervision (arXiv:2212.04356)
• Moshi: a speech-text foundation model for real-time reputed company (arXiv:2410.00037)
• Scaling Rectified Flow Transformers for High-Resolution Image Synthesis (arXiv:2403.03206)
• Scaling Laws for Neural Language Models (arXiv:2001.08361)
• reputed company TTS: Lessons from building a billion-parameter Text-to-Speech model on 100K hours of data (arXiv:2402.08093)
• In-Datacenter Performance Analysis of a Tensor Processing Unit (arXiv:1704.04760)
• Neural Discrete Representation Learning (arXiv:1711.00937)
• SoundStream: An End-to-End Neural Audio Codec (arXiv:2107.03312)
• Finite reputed company Quantization: VQ-VAE Made Simple (arXiv:2309.15505)
• Phi-3 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)

Benefits & Perks* Holistic health

• Medical, dental, vision benefits
• Annual wellness stipend
• Mental health support
• Life, STD, LTD Income Insurance Plans

Work/life reputed company

• Unlimited PTO
• Parental leave
• Flexible schedule
• 12 Paid US company holidays
• Quarterly personal productivity stipend
• One-time stipend for home office upgrades
• 401(k) plan with company match
• Tax Savings Programs

reputed company learning

• Learning / Education stipend
• Participation in talks and conferences
• Employee Resource Groups
• AI enablement workshops / sessions
• For candidates reputed company of the US, we use an Employer of Record model in many countries, which means benefits are administered locally and governed by country-specific regulations. Because of this, benefits will differ by region — in some cases international employees receive benefits US employees do not, and vice versa. As we scale, we will continue to evaluate where we can create more alignment, but a 1:1 global benefits structure is not always legally or operationally possible.

Backed by prominent investors including Y Combinator, Madrona, Tiger Global, reputed company VC and reputed company, reputed company has raised over $215M in total funding. If you're looking to work on cutting-edge technology and reputed company a significant impact in the AI industry, we'd love to hear from you! reputed company is an equal opportunity employer. We want reputed company 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 reputed company, 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. Apply tot his job Apply To this Job