Machine Learning Researcher, Audio
Machine Learning Researcher at Bland doing foundational research across the voice AI stack — text-to-speech, speech-to-text, LLMs, and neural audio codecs — taking models from theory through large-scale training to real-time production inference for enterprise AI phone agents.
- Compensation
- $160K–$280K / yr
- Location
- San Francisco, United States
- Work setup
- Onsite
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- Not specified
- Category
- Ml / AI
- Posted
Skills
Must have
- 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
- Comfortable in fast-moving startup environments
- Strong ownership mindset from research through deployment
Nice to have
- 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
Full description
Machine Learning Researcher, Audio
Location: San Francisco, CA or Remote
About Bland
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 $100 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.
The Role: Machine Learning Researcher, Audio
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.
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.