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Salary: USD 230,000 - 265,000 / annual
Otter.ai is seeking a Senior Applied Scientist to lead the design, development, and deployment of large-scale speech AI systems that power the platform's meeting transcription, summarization, and conversational intelligence features. With over 1 billion meetings transcribed, Otter is the leading meeting intelligence platform.
In this role, you will architect and evolve ASR (automatic speech recognition) and TTS (text-to-speech) systems serving millions of conversations. You'll own the full ML lifecycle—from research prototyping through production deployment, monitoring, and long-term maintenance. Key responsibilities include designing and optimizing model architectures, loss functions, decoding strategies, and training techniques using PyTorch; making principled trade-offs across quality, latency, cost, and reliability. You'll work with large-scale conversational datasets, handling data preprocessing, augmentation, quality analysis, and labeling strategies to support model training and evaluation.
You'll partner deeply with product and infrastructure teams to translate cutting-edge research into scalable, production-grade systems that deliver measurable user and business impact. You'll drive system-level improvements in model performance, robustness, observability, and operational excellence using real-world data at scale. Additionally, you'll set technical direction and best practices for ML infrastructure, data pipelines, evaluation frameworks, and deployment workflows in a cloud environment. You'll mentor and elevate other engineers, influencing team standards and contributing to a culture of strong technical decision-making.
Required qualifications include a Bachelor's or Master's degree in Computer Science or related field with 5+ years of relevant industry experience (PhD preferred). You need deep, hands-on experience building and fine-tuning speech or foundation models, with production experience in ASR and/or TTS systems. You should demonstrate strong command of modern ML research, the ability to critically evaluate new papers, and experience deploying, scaling, monitoring, and operating ML systems in production. Experience with agentic systems, tool-use frameworks, or multi-model orchestration is a plus.
