Machine Learning Engineer II

<div class="content-intro"><p>PagerDuty (NYSE:PD) is a leader in Digital Operations Management. In an always-on world, organizations of all sizes trust PagerDuty to help them deliver a perfect digital experience to their customers, every time. Teams use PagerDuty to identify issues and opportunities in real time and bring together the right people to fix problems faster and prevent them in the future. Over 13,000 organizations (including 60 of Fortune 100) rely on PagerDuty to succeed with Digital Transformation, Cloud Migration, and DevOps Modernization. Notable customers include GE, Cisco, Genentech, Electronic Arts, Cox Automotive, Netflix, Shopify, Zoom, DoorDash, Lululemon and more. We are expanding rapidly as a platform for Digital Operations Management using AI/ML and Automation and growing our adoption by Development, IT, Customer Service, Security, and other teams across the organization.</p></div><p><strong>PagerDuty</strong> is looking for a Machine Learning Engineer who is passionate about collaborating with data scientists, product managers and engineers alike. As part of our team, you will help us accelerate the development and extension of products powered by Gen AI and many other shapes of Machine Learning. You’ll be contributing hands-on to the development of the services and pipelines that enable multiple ML/AI features in our product.</p> <p>You will have the opportunity to collaborate with multiple organizations, taking input and guidance from your senior stakeholders and helping bring our initiatives to reality. You’ll succeed by showcasing excellent capacity to manage time, demonstrating emotional intelligence as you navigate stakeholder relationships, and by continuously improving your technical skill set.</p> <h3><strong>Key Responsibilities</strong></h3> <ul> <li>Build and improve the capabilities that enable and accelerate the production of machine learning (ML) and generative AI (genAI) based solutions</li> <li>Partner with data scientists, effectively sharing engineering context and collaborating to support larger initiatives</li> <li>Incorporate the best available techniques and practices to how we ship machine learning capabilities to production</li> <li>Commit to continuously optimizing our workflows and reducing technical debt</li> </ul> <h3><strong>Basic Qualifications</strong></h3> <ul> <li>3+ years of experience building, designing, and shipping machine learning solutions to production</li> <li>Proven software development track record with Python</li> <li>Demonstrated experience with data modeling, database design, extract transform load (ETL) processes, working with unstructured data, and cloud-based data infrastructure tools</li> <li>Ability to stand up infrastructure building blocks to enable ML processes like data exploration, model training and deployment)</li> </ul> <h3><strong>Preferred Qualifications</strong></h3> <ul> <li>Experience working with Product teams, ensuring and driving a timely delivery</li> <li>Exposure to large language models / Generative AI&nbsp; and understanding of the capabilities and use-cases for that technology</li> <li>Ability to develop and ship machine learning services using container orchestration systems such as kubernetes</li> </ul><div class="content-conclusion"><p><strong>Hesitant to apply?</strong></p> <p>We encourage you to submit your resume even if you don't meet every requirement. We value potential and consider each candidate's full professional story. Whether you're exploring a career change or taking your next step, we look forward to reviewing your application. If this just isn’t the right role or time - sign up for <a href="https://careers.pagerduty.com/jobalerts">job alerts</a>!</p> <p><strong>Where we work</strong></p> <p>PagerDuty operates a hybrid work model with <a href="https://careers.pagerduty.com/locations">offices</a> in 8 major cities: Atlanta, Lisbon, London, San Francisco, Santiago, Sydney, Tokyo, and Toronto. While we offer flexibility within our established locations, we <strong>cannot</strong> employ candidates residing in:</p> <p><strong>Location restrictions: </strong><strong><br></strong><strong>Australia:</strong> Northern Territory, Queensland, South Australia, Tasmania, Western Australia<br><strong>Canada:</strong> Alberta, Manitoba, Newfoundland, Northwest Territories, Nunavut, PEI, Quebec, Saskatchewan, Yukon<br><strong>United States:</strong> Alaska, Hawai