Machine Learning Engineer

The Opportunity

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 Senior/Staff 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.

Your Impact

• Architect, build, and evolve large-scale SID / ASR / NLP / LLM systems that power mission-critical product experiences including summarization, chat, and speech understanding across millions of conversations.
• Lead the design and implementation 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.
• Design and improve model architectures, loss functions, decoding strategies, and training techniques for speech and language models, informed by both research and production constraints.
• Own end-to-end ML system lifecycles, from research prototyping through production deployment, monitoring, iteration, and long-term maintenance.
• Partner deeply with product, and infrastructure teams to develop and translate cutting-edge research into scalable, production-grade systems that deliver measurable user and business impact.
• Drive system-level improvements in model performance, robustness, observability, and operational excellence using real-world conversational data at scale.
• Set technical direction and best practices for ML infrastructure, data pipelines, evaluation frameworks, and deployment workflows in a cloud environment.
• Identify and resolve complex, ambiguous problems in model behavior, data quality, scaling, and system interactions, often before they surface as user-visible issues.
• Mentor and elevate other engineers, influencing team standards, reviewing designs, and contributing to a culture of strong technical decision-making and execution.
• [staff] Influence applied research and technical roadmaps by identifying promising speech and multimodal modeling approaches, and driving their validation and adoption into production systems
  
  

We're Looking For Someone Who

• Holds a Bachelor’s or Master’s degree in Computer Science or a related field with 3 years of relevant industry experience; PhD is preferred.
• Has deep, hands-on experience building, fine-tuning, and post-training large language models or other foundation models, including an understanding of failure modes and trade-offs.
• Demonstrates strong command of modern ML research, with the ability to critically evaluate new papers and decide what is production-worthy versus experimental.
• Has interest in creating innovation and advancing applied research
• Has extensive experience deploying, monitoring, and operating ML systems in production, including model versioning, rollback strategies, and performance regression detection.
• 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 scaling ML systems across training, inference, and serving infrastructure while balancing cost, latency, and reliability constraints.
• Is highly effective at cross-functional collaboration, working end-to-end with product, infra, research, and data teams to deliver outcomes—not just models.
• Can lead technical projects independently, driving clarity in ambiguous problem spaces and making sound architectural decisions.
• Has experience with or strong interest in agentic systems, tool-use frameworks, or multi-model orchestration.
• Has significant experience with at least one of the following areas: (1) Speech recognition (ASR), (2) Text-to-speech (TTS), (3) Multimodal (speech/text) foundation models, or (4) modern LLM NLP tasks (e.g., summarization, dialogue, speech understanding), especially in real-world production settings.
• Experience with personalization, recommendation systems, or user modeling is a plus
  
  

About Otter.ai

We are in the business of shaping the future of work. Our mission is to make conversations more valuable.

With over 1B meetings transcribed, Otter.ai is the world’s leading tool for meeting transcription, summarization, and collaboration. Using artificial intelligence, Otter generates real-time automated meeting notes, summaries, and other insights from in-person and virtual meetings - turning meetings into accessible, collaborative, and actionable data that can be shared across teams and organizations. The company is backed by early investors in Google, DeepMind, Zoom, and Tesla.

Otter.ai is an equal opportunity employer. We proudly celebrate diversity and are dedicated to inclusivity.

• Otter.ai does not accept unsolicited resumes from 3rd party recruitment agencies without a written agreement in place for permanent placements. Any resume or other candidate information submitted outside of established candidate submission guidelines (including through our website or via email to any Otter.ai employee) and without a written agreement otherwise will be deemed to be our sole property, and no fee will be paid should we hire the candidate.
  
  

Salary range

Salary Range: $155,000 to $207,000 USD per year.

This salary range represents the low and high end of the estimated salary range for this position. The actual base salary offered for the role is dependent based on several factors. Our base salary is just one component of our comprehensive total rewards package.