Machine Learning Engineer at Auror, using data science to reduce retail crime through innovative ML systems. Collaborate with product teams and develop impactful solutions leveraging real - time data.
About the role
- Machine Learning Platform Engineer designing and maintaining ML infrastructure at Iterable. Collaborating with Data Science teams on AI-powered features at scale in a hybrid environment.
Responsibilities
- Lead the end-to-end development of ML pipelines, from prototyping and experimentation through to production deployment, enabling AI-powered features at scale.
- Design, build, and maintain the core infrastructure that powers our ML services (e.g., Databricks, AWS, Ray, Kubernetes).
- Develop robust observability and experimentation frameworks to ensure reliable model deployment, monitoring, and continuous iteration.
- Partner closely with Data Science teams, treating the ML Platform as a product—removing infrastructure bottlenecks and empowering them to deliver high-quality models efficiently and confidently.
Requirements
- 5+ years of experience in backend and/or infrastructure engineering.
- Strong experience with infrastructure-as-code practices (e.g., Pulumi, Terraform, CloudFormation).
- Experience working with service-oriented architectures and integrating ML services into high-scale backend systems.
- Hands-on experience building and operating ML pipelines, including traditional recommender systems and modern deep learning training and serving systems.
Benefits
- Competitive salaries & meaningful equity
- Private Medical Insurance
- Life/Risk Assurance
- Meal Allowance: 8.55€ per day
- Community Days (days for us to give back to the community)
- Paid Annual Leave (22 days)
- Global Lifestyle Reimbursement Account
- Paid Sabbatical
- Complete laptop workstation
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
No Education Requirement
Tech skills
Location requirements
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