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Audio Speech Machine Learning Jobs (NOW HIRING)

Senior Research Scientist

San Francisco, CA · On-site

$116K - $147K/yr

Deep expertise in audio and machine learning, including strong intuition for: * Speech and audio generation * Audio representations and modeling * Training large-scale neural models * Hands-on ...

Spotify is a leading audio streaming subscription service that aims to unlock the potential of human creativity. They are seeking a Machine Learning Engineer to build systems that analyze the ...

Spotify is a leading audio streaming service known for its innovative features like Blend and Discover Weekly. They are seeking a Machine Learning Engineer to join the Personalization team, focusing ...

OR

$523K - $920K/yr

Lead a broad portfolio of end-to-end initiatives in multimodal LLM and audio algorithms to achieve ... Highly proficient in multimodal LLM and speech algorithm research, and deeply committed to staying ...

... vision, audio, NLP, Generative AI). • Closely collaborate with machine learning scientists ... speech, NLP, robotics) • Highly motivated, strong communicator, with entrepreneur spirit and ...

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Audio Speech Machine Learning information

What are some common challenges faced when developing machine learning models for audio speech applications?

A key challenge in audio speech machine learning roles is dealing with diverse and noisy audio data, which can significantly affect model accuracy. Additionally, models must be robust to different accents, languages, and speaking styles, requiring large and varied datasets for training and validation. Collaboration with data engineers, linguists, and software developers is often necessary to ensure high-quality data pipelines and model integration into production systems. Staying updated with the latest research and optimizing models for real-time performance are also ongoing aspects of the role.

What is an Audio Speech Machine Learning Engineer?

An Audio Speech Machine Learning Engineer is a specialized professional who designs, develops, and implements machine learning models that process and analyze audio and speech data. Their work involves tasks like speech recognition, speaker identification, and audio event detection by leveraging algorithms and large datasets. These engineers collaborate with data scientists, software developers, and linguists to create applications such as voice assistants, transcription tools, and automated customer service systems. Expertise in signal processing, deep learning frameworks, and programming languages like Python is crucial for this role.

What is the difference between Audio Speech Machine Learning vs Speech Data Analyst?

AspectAudio Speech Machine LearningSpeech Data Analyst
Required CredentialsDegree in Computer Science, Data Science, or related fields; knowledge of ML frameworksDegree in Data Analysis, Statistics, or related fields; experience with data tools
Work EnvironmentResearch labs, tech companies, AI startupsData analysis teams, research institutions, tech firms
Industry UsageDeveloping speech recognition, voice assistants, NLP applicationsAnalyzing speech datasets, improving speech models, reporting insights

Audio Speech Machine Learning focuses on developing algorithms for speech recognition and processing, often involving model training and AI development. Speech Data Analysts interpret speech data, generate insights, and support model improvements. Both roles require strong analytical skills, but their core tasks differ: one builds models, the other analyzes data.

What are the key skills and qualifications needed to thrive as an Audio Speech Machine Learning Engineer, and why are they important?

To thrive as an Audio Speech Machine Learning Engineer, you need a solid background in machine learning, signal processing, and programming (typically Python), along with a relevant degree in computer science or a related field. Familiarity with tools like TensorFlow or PyTorch, audio processing libraries (such as Librosa), and experience with speech datasets and ASR systems are commonly required. Critical soft skills include problem-solving, innovation, and effective communication for collaborating with cross-functional teams. These skills are essential to develop accurate, scalable speech recognition systems that advance voice-driven technology.
More about Audio Speech Machine Learning jobs
What cities are hiring for Audio Speech Machine Learning jobs? Cities with the most Audio Speech Machine Learning job openings:
What states have the most Audio Speech Machine Learning jobs? States with the most job openings for Audio Speech Machine Learning jobs include:
Infographic showing various Audio Speech Machine Learning job openings in the United States as of May 2026, with employment types broken down into 50% Internship, and 50% Full Time. Highlights an 100% In-person job distribution.
Research Engineer, Machine Learning Systems

Research Engineer, Machine Learning Systems

Deepgram

San Francisco, CA • On-site, Remote

$150K - $250K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 24 days ago


Job description

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
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 seeking a highly skilled and versatile Machine Learning Engineer to join our Research team. As a Member of the Research Staff, you will partner with research scientists to prototype and validate novel modeling ideas, then scale them through robust training systems for speech technologies, internal tooling, and innovative data strategies. You'll work at the intersection of machine learning, data infrastructure, and internal tooling to support our mission of building world-class speech recognition and synthesis systems. On the Research team, you will experiment with new technologies and techniques, while also working on product-focused deliverables, learning from colleagues with a wide range of expertise in AI and machine learning as you go.
Key Responsibilities
  • Scalable Model Training: Architect and manage horizontally scalable systems that dramatically accelerate the end-to-end training lifecycle for Speech-to-Text (STT) and Text-to-Speech (TTS) models. This includes far more than automated training: the role focuses on making model development significantly faster and more efficient through optimized data preparation and management, high-throughput training pipelines, distributed infrastructure, and automated evaluation tooling.
  • Tooling & Accessibility: Design and implement internal UIs and tools that make ML systems and workflows accessible to non-technical stakeholders across the company. These UIs should be designed to provide transparency and flexibility to internally built tooling.
  • Infrastructure & Tools: Oversee and manage training tooling, job orchestration, experiment tracking, and data storage.
The Challenge
We are seeking Members of the Research Staff 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.
It's Important to Us That You Have
  • Strong experience with the machine learning research pipeline, particularly in STT or related speech domains. This includes experimenting with and evaluating new architectures and modeling approaches, and implementing large-scale training systems.
  • Proficiency with orchestration and infrastructure tools like Kubernetes, Docker, and Prefect.
  • Familiarity with ML lifecycle tools such as MLflow.
  • Experience building internal tools or dashboards for non-technical users.
  • Hands-on experience with data engineering practices for unstructured audio and text data.
  • Comfortable working in cross-functional teams that include researchers, engineers, and product stakeholders.
Why Join Deepgram?
At Deepgram, you'll help shape the future of human-machine communication. Our research culture prioritizes ownership, experimentation, and real-world impact. As a Member of the Research Staff, you'll be empowered to build tools and systems that accelerate ML research and product deployment at scale.
Benefits & Perks*
Holistic health
  • Medical, dental, vision benefits
  • Annual wellness stipend
  • Mental health support
  • Life, STD, LTD Income Insurance Plans

Work/life blend
  • Unlimited PTO
  • Generous paid 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

Continuous learning
  • Learning / Education stipend
  • Participation in talks and conferences
  • Employee Resource Groups
  • AI enablement workshops / sessions

*For candidates outside 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, Wing VC and NVIDIA, Deepgram has raised over $215M in total funding. If you're looking to work on cutting-edge technology and make a significant impact in the AI industry, we'd 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.