Bland - Machine Learning Researcher, Audio

• 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. • Builder Mentality • Comfortable in fast-moving startup environments. • Strong ownership mindset from research through deployment. • Excited by ambiguous, unsolved problems. • How You Show Up • You treat unsolved problems as opportunities to invent new paradigms. • You identify the single experiment that can validate an idea in days, not months. • You measure everything and let data drive decisions. • You are obsessed with making voice agents sound truly human. • You use AI tools aggressively to amplify your own impact and accelerate research cycles.