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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 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. We're Looking for Someone Who 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. 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. 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. Has experience deploying, scaling, monitoring, and operating ML systems in production across training, inference, and serving infrastructure. 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. 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. 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: $230,000 to $265,000 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. #LI-Hybrid
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.