Copy of Machine Learning Researcher, Audio

What You Will Do

Build and Scale Next-Generation TTS Systems

• Design and train large scale text-to-speech models capable of expressive, controllable, human-sounding output.
• Develop neural audio codec-based TTS architectures for efficient, high-fidelity generation.
• Improve prosody modeling, question inflection, emotional expression, and multi-speaker robustness.
• Optimize for real-time, low-latency inference in production.

Advance Speech-to-Text Modeling

• Build and fine-tune large scale ASR systems robust to accents, noise, telephony artifacts, and code switching.
• Leverage self-supervised pretraining and large-scale weak supervision.
• Improve transcription accuracy for real-world enterprise scenarios, including structured extraction and conversational nuance.

Pioneer Neural Audio Codecs

• Research and implement neural audio codecs that achieve extreme compression with minimal perceptual loss.
• Explore discrete and continuous latent representations for scalable speech modeling.
• Design codec architectures that enable downstream generative modeling and controllable synthesis.

Develop Scalable Training Pipelines

• Curate and process massive audio datasets across languages, speakers, and environments.
• Design staged training curricula and data filtering strategies.
• Scale training across distributed GPU clusters focusing on cost, throughput, and reliability.

Run Rigorous Experiments

• Design ablation studies that isolate the impact of architectural changes.
• Measure improvements using both objective metrics and perceptual evaluations.
• Validate ideas quickly through focused experiments that confirm or eliminate hypotheses.

What Makes You a Great Fit

Deep Research Foundations

• Experience with self-supervised learning, multimodal modeling, or generative modeling.
• Ability to derive new formulations and implement them efficiently.

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, including quantization, kernel optimization, and memory efficiency.
• 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.

Bonus Points

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

Benefits and Compensation

• Healthcare, dental, vision.
• Meaningful equity in a fast-growing company.
• Every tool you need to succeed.
• Beautiful office in Jackson Square, SF with rooftop views.
• Competitive salary: $160,000 to $250,000.