[Hiring] Machine Learning Researcher, Audio @Bland

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description As a Machine Learning Researcher at Bland, you'll be working on foundational research and development across the core components of our voice stack: speech-to-text, large language models, neural audio codecs, and text-to-speech. Your work will define how our agents understand, reason, and speak in real time at enterprise scale. • 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. Qualifications • Experience with self-supervised learning, multimodal modeling, or generative 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. • Experience training and serving large models on modern accelerators. • Track record of designing controlled experiments and meaningful ablations. • Comfortable in fast-moving startup environments. Requirements • Ability to derive new formulations and implement them efficiently. • Strong intuition for audio quality, prosody, and conversational dynamics. • Knowledge of inference optimization techniques, including quantization, kernel optimization, and memory efficiency. • Understanding of real-time constraints in telephony or streaming environments. • Ability to move quickly from hypothesis to validation. • Strong ownership mindset from research through deployment. • Excited by ambiguous, unsolved problems. Benefits • Healthcare, dental, vision, all the good stuff • 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 Apply tot his job Apply To this Job