Machine Learning Researcher, Audio

About this role

Bland is seeking an ML researcher to develop foundational audio technologies for enterprise AI phone agents, including speech-to-text, text-to-speech, and neural audio codecs. You'll move research from theory through large-scale training to production systems serving millions of daily calls, collaborating across research and engineering teams.

What you'll do

• Design and train large-scale text-to-speech models with expressive, controllable output and optimize for real-time inference
• Build robust speech-to-text systems handling accents, noise, telephony artifacts, and code switching
• Research and implement neural audio codecs achieving high compression with minimal perceptual loss
• Develop and scale distributed training pipelines for massive multilingual audio datasets
• Design rigorous ablation studies and experiments with both objective metrics and perceptual evaluations
• Curate audio datasets and implement data filtering and staged training strategies

What they're looking for

• Self-supervised learning and generative modeling
• Text-to-speech (TTS) system development and scaling
• Automatic speech recognition (ASR) and robustness techniques
• Neural audio codecs and audio compression
• Distributed GPU training and large-scale ML pipelines
• Experimental design and rigorous evaluation methodology
• Audio signal processing and multimodal modeling
• Python and deep learning frameworks

Benefits

• Remote or San Francisco office location
• Work on foundational voice AI research at scale
• Collaborate with well-funded company backed by leading Silicon Valley investors
• Opportunity to impact enterprise customer interactions through voice technology