Machine Learning Engineer

<div> <p><strong>The Opportunity</strong><strong><br></strong>Do you want to lead projects to build and deploy cutting-edge AI technology to help people get unparalleled value from meetings and conversations? Join our core AI team responsible for ML and work alongside industry-veteran scientists and engineers. As a Machine Learning Engineer, you’ll bring your strong software engineering mindset to machine learning in order to scale and optimize our ML systems—creating and transforming innovative research into production-ready features that power Otter’s summarization and conversational intelligence products.</p> <p><strong>Your Impact</strong></p> <ul> <li><strong>Architect, build, and evolve</strong> large-scale SID / ASR / NLP / LLM systems that power mission-critical product experiences including summarization, chat, and speech understanding across millions of conversations.</li> <li><strong>Lead the design and implementation</strong> of training, fine-tuning, post-training, and inference strategies for large language and speech models using PyTorch and/or JAX, making principled trade-offs across quality, latency, cost, and reliability.</li> <li><strong>Design and improve model architectures,</strong> loss functions, decoding strategies, and training techniques for speech and language models, informed by both research and production constraints.</li> <li><strong>Own end-to-end ML system lifecycles</strong>, from research prototyping through production deployment, monitoring, iteration, and long-term maintenance.</li> <li><strong>Partner deeply with product, and infrastructure teams</strong> to develop and translate cutting-edge research into scalable, production-grade systems that deliver measurable user and business impact.</li> <li><strong>Drive system-level improvements</strong> in model performance, robustness, observability, and operational excellence using real-world conversational data at scale.</li> <li><strong>Set technical direction and best practices</strong> for ML infrastructure, data pipelines, evaluation frameworks, and deployment workflows in a cloud environment.</li> <li><strong>Identify and resolve complex, ambiguous problems</strong> in model behavior, data quality, scaling, and system interactions, often before they surface as user-visible issues.</li> <li><strong>Mentor and elevate other engineers</strong>, influencing team standards, reviewing designs, and contributing to a culture of strong technical decision-making and execution.</li> </ul> <p><strong>We're Looking for Someone Who</strong></p> <ul> <li>Holds a <strong>Bachelor’s or Master’s degree in Computer Science or a related field with 3+ years of relevant industry experience</strong>; PhD is preferred.</li> <li>Has <strong>deep, hands-on experience</strong> building, fine-tuning, and post-training large language models or other foundation models, including an understanding of failure modes and trade-offs.</li> <li>Demonstrates <strong>strong command of modern ML research</strong>, with the ability to critically evaluate new papers and decide what is production-worthy versus experimental.</li> <li>Has interest in creating innovation and advancing <strong>applied research</strong></li> <li>Has <strong>extensive experience deploying, monitoring, and operating ML systems in production</strong>, including model versioning, rollback strategies, and performance regression detection.</li> <li>Is comfortable working with <strong>large-scale speech and conversational datasets</strong>, including data preprocessing, augmentation, quality analysis, and labeling strategies to support model training and evaluation.</li> <li>Has experience <strong>scaling ML systems</strong> across training, inference, and serving infrastructure while balancing cost, latency, and reliability constraints.</li> <li>Is highly effective at <strong>cross-functional collaboration</strong>, working end-to-end with product, infra, research, and data teams to deliver outcomes—not just ... (truncated, view full listing at source)