Xero

Xero

Staff Machine Learning Engineer

CAN: British Columbia Remote · Staff+

Sponsorship not specified$195k-$235kDetected 4 days ago
PythonAWSCloud PlatformsKubernetesTemporalMachine LearningLLMsAgentic AIAgileResearchCommunicationCollaboration

About the role

  • You will take ambiguous, complex R&D problems and convert them into production-ready software, ensuring our models are robust, scalable, and secure.
  • The team / how they connect You will join the AI North America team, a highly collaborative and innovative group of around 40 people working remotely across Canada and the US in the PST timezone.
  • The team consists of applied scientists and engineers who work together closely on cutting-edge Agentic AI implementations within an agile framework.

Responsibilities

  • Developing JAX 3.0, transitioning our chatbot into a next-generation, AI-native platform for work
  • A strong background in building highly scalable backend engineering solutions in cloud environments

Requirements

  • Building the infrastructure and workflows required for agentic performance and evaluation

Compensation

  • Variable Pay: Permanent employees are eligible to participate in our annual bonus and equity (RSU) programs. You may also be eligible for performance-based cash or equity (RSUs) incentives depending on your role level, and company performance.

Benefits

  • World-class health, wellness, and retirement programs.
  • Xero Perks https://careers.xero.com/inside-xero/benefits-wellbeing/ including Wellbeing days, generous leave, and dedicated professional development budgets.
  • Permanent employees are eligible to participate in our annual bonus and equity (RSU) programs.
  • You may also be eligible for performance-based cash or equity (RSUs) incentives depending on your role level, and company performance.
  • Familiarity with Python, Kubernetes, GenAI, machine learning concepts, or frontend interfaces would be a welcome addition to the team

This listing is sourced directly from Xero's careers page and normalized into a canonical job model.