Research Engineer - Audio & Speech Models

Benefits

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Market context

U.S. role benchmark (BLS OEWS)

$116,543 U.S. median for this role

Projected growth (BLS Employment Projections)

+9.8% - Much faster than average

Matched to SOC 15-1252 - Software Engineering aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Engineering From the posting source checked Jun 20, 2026

Seniority

Mid From the posting source checked Jun 20, 2026

Schedule

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Weekend work

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About this role

Research Engineer - Audio & Speech Models San Francisco, California, United States Zyphra is an artificial intelligence company based in San Francisco, California. The Role: As a Research Engineer - Audio & Speech Models , you will be a core contributor on Zyphra's Audio Team, building the next generation of open-source autoencoders, ASR, TTS, SSL, and speech-to-speech models. You will be deeply involved in the entire model training process, from data gathering and processing to designing novel architectures and training methodologies. You'll Work Across: - Large-scale audio training runs - Performance optimization of our training stack - Audio dataset collection, processing, and evaluation - Architecture and training methodology ablations and improvements What We're Looking For / Requirements: - Strong research taste and intuition. The ability to work through a research project from conception to execution to write-up. - Strong implementation and prototyping ability (can take an idea from conception to experimentation quickly) - The ability to work well with others in a high-paced research setting - Excellent communication and collaboration skills, and can work effectively on both research and engineering implementation at scale Qualifications / Additional Skills: - Expertise and intuition for training models in the audio domain, including text-to-speech, ASR, speech-to-speech, speech-emotion-recognition, or other models - Experience in training audio autoencoders - Understanding of signal processing, especially of audio signals - Experience with diffusion models, consistency models, or GANs - Experience with training on large-scale (multi-node) GPU clusters - Strong grasp of proper experimental methodology for running rigorous ablations

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