Staff Machine Learning Engineer, PagerDuty

• You have 8+ years of experience building, designing, and evolving data architecture for large-scale systems
• Excellent communication skills
• Experience working with Product teams, ensuring and driving a timely delivery
• Have a deep understanding of the trade-offs to be considered when designing and delivering machine learning solutions to production
• Experience leading cross-team architecture discussions, building technical prototypes, and driving the adoption of best practices across diverse teams
• Demonstrated experience with data engineering processes, working with unstructured data and cloud-based data infrastructures
• Passionate about ML engineering and interested in driving discussions with stakeholders and executives

Desirable

• We are on the hunt for a formidable Staff Machine Learning (ML) Engineer who is not only adept at architecting state-of-the-art AI initiatives but who is also ready to mentor and inspire a team composed of Machine Learning, Data Scientists, and Software Engineers
• In this role, you will actively contribute to and lead AI-first innovation across our customers' Operations powered by ML and Agentic AI solutions
• You will be instrumental in developing groundbreaking proofs of concept, contributing to our AI strategy, and pushing the boundaries of what's possible—all while addressing critical short and long-term team goals
• In a world where customer adoption of new technologies is uncertain and competition is fierce, we need Staff Engineering leaders who thrive under pressure, are capable of operating at different levels of altitude, and pave the way for a future where our products not only meet but exceed the aspirations of our existing and future customers
• Build and improve the capabilities of the data platform that enable and accelerate the production of ML/AI-based solutions
• Drive and define standards for AI/ML across the organization
• Provide guidance, technical leadership, and mentoring to other members of the team
• Mentor junior members and participate in scaling up the existing team
• Proactively recommend improvements and new approaches addressing potential systemic pain points and technical debt
• Anticipate technical demands on the data platform based on the organization’s roadmap and systematically drive the evolution of the architecture toward those ends
• Develop a long-term plan for ML/AI investments