Scheduling isn’t simply filling shifts. It’s finding the sweet spot that enables businesses to grow and team members to enjoy the perfect work/life balance.
At Planday from Xero, we aim to use Agentic AI to build a future where managers can seamlessly free up invaluable time for their business and teams. We’re not just building software: we’re on a mission to make shift work more human, to change work/life balance from a luxury to a reality for all shift workers. We’re using advanced technology to help humans reach their full potential. At work and in life.
Founded in 2004, Planday is headquartered in Copenhagen, Denmark and helps create perfect schedules for hundreds of thousands of users across the world.
How You’ll Make an Impact
As a Machine Learning Engineer, you’ll play a key role in shaping intelligent, human-centered features across the Planday platform. You’ll apply your ML expertise to complex business domains like scheduling, onboarding, and payroll — helping build tools that are as smart as they are impactful.
You’ll be part of a cross-functional, collaborative team working on high-impact initiatives in a supportive, innovation-driven environment.

WHAT WE’RE LOOKING FOR

• 
  
  
  
• We’re seeking someone with solid, hands-on experience in applied machine learning and a strong foundation in modern ML techniques and tooling. You don’t need to be an expert in everything, but you should be eager to learn and ready to contribute.Proven experience developing ML models in real-world scenarios

• Proficiency in Python and core ML frameworks
• Strong grasp of statistics and core ML techniques — both traditional and generative
• Experience with LLMOps practices such as prompt engineering, deployment, and lifecycle management of large language models
• Strong understanding of model evaluation methodologies and experience building robust evaluation solutions
• Familiarity with MLOps tooling for monitoring, automation, and reproducibility
• Bonus: Experience with .NET (C#), Azure, or JavaScript/TypeScript

• 
  
  
  
• Understand complex domains: Investigate challenges in scheduling, staffing, onboarding, and compliance

• Prototype and test: Build ML pipelines and run experiments to evaluate and refine model performance
• Deploy at scale: Bring your solutions to life in production using modern ML and LLMOps tooling
• Collaborate and co-create: Work closely with product managers, engineers, and designers to ship useful, intelligent features
• Contribute to team growth: Share learnings, contribute to best practices, and grow alongside a curious, motivated team