Machine Learning Researcher, Audio

About the role

The Machine Learning Researcher, Audio role at Bland is dedicated to advancing the technology behind voice interfaces for businesses. Located in San Francisco or remotely, this position involves foundational research and development across core voice stack components such as speech-to-text, large language models, neural audio codecs, and text-to-speech. The ideal candidate will contribute to defining how these systems operate at enterprise scale, from theoretical models to practical applications.

Responsibilities

• Build and scale next-generation TTS systems, developing and fine-tuning large-scale text-to-speech models for expressive, controllable, and human-sounding output.
• Design and train neural audio codec-based TTS architectures for efficient, high-fidelity generation, improving prosody modeling, question inflection, emotional expression, and multi-speaker robustness.
• Optimize for real-time, low-latency inference in production environments.
• Pioneer neural audio codecs, researching and implementing models that achieve extreme compression with minimal perceptual loss, exploring discrete and continuous latent representations for scalable speech modeling.
• Develop scalable training pipelines, curating and processing massive audio datasets across languages, speakers, and environments, designing staged training curricula and data filtering strategies.
• Run rigorous experiments, designing ablation studies that isolate the impact of architectural changes, and measuring improvements using both objective metrics and perceptual evaluations.

Requirements

Deep research foundations, experience with self-supervised learning, multimodal modeling, or generative modeling. Expertise in voice modeling, hands-on experience building or scaling TTS, STT, or neural audio codec systems. Familiarity with large scale speech datasets and real-world audio variability. Strong intuition for audio quality, prosody, and conversational dynamics. Systems and hardware awareness, experience training and serving large models on modern accelerators, knowledge of inference optimization techniques, understanding of real-time constraints in telephony or streaming environments. Experimental rigor, track record of designing controlled experiments and meaningful ablations, comfortable working with both offline benchmarks and live production metrics. Ability to move quickly from hypothesis to validation. Builder mentality, comfortable in fast-moving startup environments, strong ownership mindset from research through deployment. Excited by ambiguous, unsolved problems.

Qualifications

• PhD in ML, AI, or a related field, or equivalent research impact.

Skills

• Experience with large scale distributed training.
• Research publications or open source contributions in speech or language AI.
• Background in real-time speech systems or telephony.

Benefits and Compensation

• Healthcare, dental, vision benefits.
• Meaningful equity in a fast-growing company.
• Beautiful office in Jackson Square, SF with rooftop views.
• Competitive salary range: $140K - $250K.