Machine Learning Research Scientist / Engineer, Audio

Machine Learning Research Scientist / Engineer, Audio

Scale partners with the world's leading AI labs to accelerate progress in frontier generative AI. We are building a dedicated research team focused on pushing the boundaries of speech generation, speech recognition, and speech-to-speech transformation. We are hiring Research Scientists and Research Engineers with deep expertise across text-to-speech (TTS), speech-to-speech (STS), and automatic speech recognition (ASR) to help define the next era of human-AI communication.

In this role, you will invent and deploy new algorithms to expand the capabilities, fidelity, and generalization of large-scale audio models. You will shape research directions at the intersection of modeling, data, and evaluation for speech systems. You will collaborate with world-class researchers and directly contribute technical and strategic insights to the development of the next generation of open and proprietary foundation models in audio. We encourage collaborations within the industry and academia, and support the publication of research findings. Successful candidates will partner with top foundation model labs, providing both technical and strategic input on the development of the next generation of generative AI models.

You will :

• Develop novel training and post-training techniques for audio models, advancing core areas such as speaker adaptation, prosody control, noise robustness, etc.
• Design new reward models and preference optimization techniques specifically for speech quality, emotion, and intelligibility in TTS, STS, and ASR.
• Investigate model failure modes in realistic deployment settings and propose scalable solutions for bias mitigation, robustness to accents, and long-tail speaker variation.
• Create industry-standard evaluations for the world's leading speech models
• Publish breakthrough research at leading conferences and shape best practices across the speech research community.
• Ph.D. or Master's degree in Computer Science, Machine Learning, Electrical Engineering, or a related field with a focus on speech or audio processing
• Deep understanding of neural generative models for audio, including recent advances in transformer-based architectures, diffusion models, and large-scale pretraining for speech.
• Experience with fine-tuning, reinforcement learning, or reward modeling for audio quality optimization or speech preference modeling.
• A strong research portfolio with publications at top venues such as Interspeech, NeurIPS, ICLR, ICML, ACL, or similar.
• Exceptional written and verbal communication skills, with the ability to clearly convey complex technical ideas to both internal and external partners.
• Prior experience working directly with model training, data curation, or evaluation at scale is a significant plus.

Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position, determined by work location and additional factors, including job-related skills, experience, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You'll also receive benefits including, but not limited to : Comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend.

Please reference the job posting's subtitle for where this position will be located. For pay transparency purposes, the base salary range for this full-time position in the locations of San Francisco, New York, Seattle is :

200,000 - $325,000 USD

PLEASE NOTE : Our policy requires a 90-day waiting period before reconsidering candidates for the same role. This allows us to ensure a fair and thorough evaluation of all applicants.

About Us :

At Scale, we believe that the transition from traditional software to AI is one of the most important shifts of our time. Our mission is to make that happen faster across every industry, and our team is transforming how organizations build and deploy AI. Our products power the world's most advanced LLMs, generative models, and computer vision models. We are trusted by generative AI companies such as OpenAI, Meta, and Microsoft, government agencies like the U.S. Army and U.S. Air Force, and enterprises including GM and Accenture. We are expanding our team to accelerate the development of AI applications.

We believe that everyone should be able to bring their whole selves to work, which is why we are proud to be an inclusive and equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability status, gender identity or Veteran status.

We are committed to working with and providing reasonable accommodations to applicants with physical and mental disabilities. If you need assistance and / or a reasonable accommodation in the application or recruiting process due to a disability, please contact us at accommodations@scale.com. Please see the United States Department of Labor's Know Your Rights poster for additional information.

PLEASE NOTE : We collect, retain and use personal data for our professional business purposes, including notifying you of job opportunities that may be of interest and sharing with our affiliates. We limit the personal data we collect to that which we believe is appropriate and necessary to manage applicants' needs, provide our services, and comply with applicable laws. Any information we collect in connection with your application will be treated in accordance with our internal policies and programs designed to protect personal data. Please see our privacy policy for additional information.

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