Senior Machine Learning Engineer

Join Otter, a leading company in conversational intelligence and summarization technology. As a Senior Machine Learning Engineer, you will be responsible for architecting, building, and evolving large-scale machine learning systems that power mission-critical product experiences. You will lead the design and implementation of training and inference strategies for large language and speech models, own end-to-end ML system lifecycles, and drive system-level improvements in model performance and operational excellence. You will also mentor and elevate other engineers, influencing team standards and contributing to a culture of strong technical decision-making and execution.

• Architect, build, and evolve large-scale SID / ASR / NLP / LLM systems that power mission-critical product experiences.
• Lead the design and implementation of training, fine-tuning, post-training, and inference strategies for large language and speech models.
• Own end-to-end ML system lifecycles, from research prototyping through production deployment, monitoring, iteration, and long-term maintenance.
• Do you want to lead projects to build and deploy cutting-edge AI technology to help people get unparalleled value from meetings and conversations?
• Can lead technical projects independently, driving clarity in ambiguous problem spaces and making sound architectural decisions
• Demonstrates strong command of modern ML research, with the ability to critically evaluate new papers and drive innovation by identifying what is production-worthy versus experimental
• Holds a Bachelor’s or Master’s degree in Computer Science or a related field with 5+ years of relevant industry experience; PhD is preferred
• Is comfortable working with large-scale speech and conversational datasets, including data preprocessing, augmentation, quality analysis, and labeling strategies to support model training and evaluation
• Has experience with personalization, recommendation systems, or user modeling is a plus
• Has experience with or strong interest in agentic systems, tool-use frameworks, or multi-model orchestration
• Has deep, hands-on experience building and fine-tuning large language or foundation models, with production experience in ASR, TTS, multimodal, or modern LLM/NLP systems, and a strong understanding of model failure modes and trade-offs
• Is highly effective at cross-functional collaboration, working end-to-end with product, infra, research, and data teams to deliver outcomes, not just models
• Has extensive experience deploying, scaling, monitoring, and operating ML systems in production across training, inference, and serving infrastructure, including model versioning, rollback strategies, and performance regression detection while balancing cost, latency, and reliability constraints