Senior Machine Learning Engineer

Calendly
Remote - USPosted 20 March 2026

Job Description

What’s in it for you? Ready to make a serious impact? Millions of people already rely on Calendly, and we’re still in the midst of exciting product growth — it’s a fantastic time to join us. Everything you’ll work on here will accelerate your career to the next level. If you want to learn, grow, and do the best work of your life alongside the best people you’ve ever worked with, then we hope you’ll consider allowing Calendly to be a part of your professional journey. About the team opportunity What’s so great about working on Calendly’s Data Science Machine Learning team? We make things possible for our customers through innovation in data, analytics and AI. Why do we need you? Well, we are looking for a Machine Learning Engineer who will deliver business value by building new AI Products for Calendly’s customers. You will report to the head of AI and will be responsible for building and operating AI-powered features that create magical experiences for our customers. Our team: Builds new AI products for Calendly’s customers, bringing new capabilities to life. Works closely with product, design, marketing, customer success, and engineering teams to implement AI solutions to real customer problems. Drives innovation and makes an impact. You will join a high performing AI team and be an integral part of building new, AI based experiences for internal and external customers alike. What you’ll do On a typical day, you’ll own features end to end within our AI ecosystem, with growing independence and impact. Own AI powered features from design through deployment, partnering with product, design, and engineering to scope work and define success metrics. Understand and share domain knowledge, answering domain specific questions for your product area and documenting what you learn for the team. Prioritize your work independently, balancing feature development, quality, and maintenance, and communicating tradeoffs clearly. Proactively seek and offer support to teammates pairing, reviewing, and collaborating to move projects forward. Understand and troubleshoot our deployment pipelines, including build, test, and release steps for ML services and data pipelines. Use our monitoring and observability tools to effectively triage alerts and incidents, collaborating with partners to restore service and prevent recurrence, and participate in the team’s on-call rotation and incident response. Serve as a subject matter expert for the features and services you own, including their data contracts, SLAs, and dependencies. Be a frequent user of AI Tools and champion of adoption to the rest of the company. What do we need from you? 6+ years of industry experience in applied AI/ML and software engineering with a demonstrated track record of shipping and operating AI /ML applications in production. Experience working on GenAI products architecture/system design and implementation. Hands-on experience implementing AI/ML solutions for high-traffic, low-latency, large-data applications that produced tangible impact for end users. Understanding of foundation models and the open-source ecosystem, including model fine-tuning and prompt engineering for real product use cases. Strong programming (Python / Scala / Java / SQL etc) and data engineering skills. High level of ownership, and the ability to find a way to success. You have strong verbal and written communication skills. Authorized to work lawfully in the United States of America as Calendly does not engage in immigration sponsorship at this time What’s in it for you? Ready to make a serious impact? Millions of people already rely on Calendly’s products, and we’re still in the midst of our growth curve — it’s a fantastic time to join us. Everything you’ll work on here will accelerate your career to the next level. If you want to learn, grow, and do the best work of your life alongside the best people you’ve ever worked with, then we hope you’ll consider allowing Calendly t ... (truncated, view full listing at source)
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