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About the Role
We are looking for a Senior Machine Learning (ML) Engineer for the Menu Personalization team, someone who helps build and operate the recommender stack that runs in production. You will design, build, and operate ML systems across feature pipelines, training workflows, model serving, experimentation tooling, and the infrastructure underneath, owning significant parts of the stack end to end. You will be expected to bring a point of view on how we improve personalization and to back it with data and user evidence. You will partner with Data Scientists to take models from notebook to production, with Data Engineers on features and pipelines, with Backend Engineers on online inference paths, and with Product on what to build next.
Responsibilities
- Build and operate ML systems for menu personalization, including feature pipelines, training workflows, model serving, experimentation tooling, and infrastructure.
- Translate research and experiments into reliable production systems, collaborating with Data Scientists to meet latency, scalability, and observability requirements.
- Own significant components of the recommender stack end-to-end, from design to deployment and ongoing operation.
- Operate and improve deployed systems, focusing on instrumentation and continuous learning.
- Contribute to the personalization roadmap with Product and Engineering, supported by data and user evidence.
- Elevate the team's technical standards through code and design reviews, mentorship, and demonstrating production ML best practices.
Requirements
- 5+ years building and operating production ML systems.
- Production experience with recommender systems or large-scale personalization.
- Fluency in data and ML stack (Python, Spark) and working knowledge of backend and platform stack (Go, Kafka, Kubernetes), with hands-on experience in pipelines, model serving, and observability at scale.
- Statistical literacy for designing experiments and evaluating model performance.
- Operational judgment to diagnose system issues, identify root causes, and deploy debuggable systems under load.
- Hands-on experience with AI tooling (Claude Code, Cursor, Copilot) and practical understanding of context's impact on output quality.
- Strong product sense, with the ability to define and justify features based on evidence and business value.
- Bias towards shipping, taking full ownership to complete tasks.
Skills
- Python
- Spark
- Go
- Kafka
- Kubernetes
- ML systems
- Recommender systems
- Large-scale personalization
- Statistical literacy
- Operational judgment
- AI tooling (Claude Code, Cursor, Copilot)
- Product sense
- Ownership
Location
- Toronto, ON
- Hybrid
Work Type
- Hybrid
Experience Level
- Senior
- 5+ years of experience
Salary/Compensations
- $122.74 - $132.74 CAD
Benefits
- 75% discount on weekly HelloFresh and Chefs Plate meal kits
- 50% off weekly Factor meal box
- Health and Dental benefits from day 1
- Health Spending Account
- Unlimited access to Headspace app
- 25% discount on GoodLife fitness memberships
- Generous vacation and PTO
- Parental leave top-up program
- Career progression support and investment in continued learning
- Team socials and engaging company days
- Diversity, Equity & Inclusion initiatives (FreshPride, Women Empowered, LIMES ERGs)
- Food puns and punny meeting room names
About the Company
- HelloFresh aims to change how people eat by offering high-quality food and recipes. A global food solutions group and the world's leading meal kit company, active in 18 countries across 3 continents. The HelloFresh Group includes HelloFresh, Green Chef, EveryPlate, Chefs Plate, Factor_, YouFoodz, The Pets Table, and GoodChop.
- The Menu Personalization team owns recommender systems matching customers to recipes across global markets, comprising Data Scientists, Backend Engineers, Data Engineers, ML Engineers, and Product. The team focuses on shaping customer experience and business growth through personalization. Engineers are empowered to own customer problems, validate solutions with data, and ship using AI.