Copy of Machine Learning Researcher, Audio

Bland•San Francisco, CA

1d•Hybrid

About The Position

At Bland.com, our mission is to empower enterprises to build AI phone agents at scale. Based in San Francisco, we are a fast-growing team reimagining how customers interact with businesses through voice. We have raised $65 million from leading Silicon Valley investors, including Emergence Capital, Scale Venture Partners, Y Combinator, and founders of Twilio, Affirm, and ElevenLabs. Voice is quickly becoming the primary interface between businesses and their customers. We are building the models and infrastructure that make those interactions feel natural, reliable, and genuinely human. 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. This is not a narrow research role. You will take ideas from theory to large-scale training to production inference systems serving millions of calls per day. You will design new modeling approaches, validate them with rigorous experimentation, and collaborate with engineering teams to deploy them into real customer environments.

Requirements

Experience with self-supervised learning, multimodal modeling, or generative modeling.
Ability to derive new formulations and implement them efficiently.
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.
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.
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.
Comfortable in fast-moving startup environments.
Strong ownership mindset from research through deployment.
Excited by ambiguous, unsolved problems.

Nice To Haves

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.

Responsibilities

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.
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.
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.
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.
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.