Staff Machine Learning Engineer

Join Otter, a leading company in summarization and conversational intelligence products. As a Staff Machine Learning Engineer, you will architect, build, and evolve large-scale systems that power mission-critical product experiences. You will lead the design and implementation of training 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 other engineers and influence applied research and technical roadmaps.

• 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.
• 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 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
• 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
• Experience with personalization, recommendation systems, or user modeling is a plus
• 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
• 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 demonstrated ability to influence beyond their immediate team, shaping technical direction, standards, or long-term strategy
• Holds a Bachelor’s or Master’s degree in Computer Science or a related field with 10+ years of relevant industry experience; PhD is preferred
• Has experience with or strong interest in agentic systems, tool-use frameworks, or multi-model orchestration