Machine Learning Engineer II
PagerDuty 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.
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.
Key Responsibilities
Build and improve the capabilities that enable and accelerate the production of machine learning (ML) and generative AI (genAI) based solutions
Partner with data scientists, effectively sharing engineering context and collaborating to support larger initiatives
Incorporate the best available techniques and practices to how we ship machine learning capabilities to production
Commit to continuously optimizing our workflows and reducing technical debt
Basic Qualifications
3+ years of experience building, designing, and shipping machine learning solutions to production
Proven software development track record with Python
Demonstrated experience with data modeling, database design, extract transform load (ETL) processes, working with unstructured data, and cloud-based data infrastructure tools
Ability to stand up infrastructure building blocks to enable ML processes like data exploration, model training and deployment)
Preferred Qualifications
Experience working with Product teams, ensuring and driving a timely delivery
Exposure to large language models / Generative AI and understanding of the capabilities and use-cases for that technology
Ability to develop and ship machine learning services using container orchestration systems such as kubernetes