Staff Backend Engineer - ML Model Serving & Production

hellofresh; United States; Hybrid; USD 107,000 - 115,000; Full time

Benefits offered by this job

Health and dental coverage from day 1

Mental wellness app subscription

Fitness membership discounts

Generous vacation and PTO

Job summary

hellofresh is hiring a Staff Backend Engineer for a Customer Value Optimization team in Toronto. You will translate ML model needs into scalable backend systems and serve as a technical bridge between data science and engineering across multiple squads.

You will shape model serving practices, drive scalable architectures, and mentor engineers while delivering high-impact, production-grade ML platforms in a hybrid work setting.

Qualifications

• 7+ years of backend engineering with Python
• Experience building/operating ML model serving systems
• Strong understanding of distributed systems, APIs and microservices
• Proven track record delivering cross-functional projects
• Excellent communication with data scientists and engineers
• Collaborative, low-ego approach with strong technical judgment

Responsibilities

• Design, build, and own robust infrastructure for live serving of ML models in production
• Translate data science needs into engineering solutions and bridge DS and backend teams
• Define and drive best practices for model serving, APIs, and system design across squads
• Lead cross-squad technical initiatives from architecture to implementation
• Partner with data scientists to ensure production-ready models are performant and observable
• Contribute to engineering standards and the tribe's technical roadmap
• Mentor engineers across squads without line-management duties

Skills

Backend Python

Distributed systems

APIs

Microservices

Cross-functional delivery

Communication

Tools

ML frameworks

Serving tools

Feature stores

Job description

hellofresh is hiring a Staff Backend Engineer for a Customer Value Optimization team in Toronto. You will translate ML model needs into scalable backend systems and serve as a technical bridge between data science and engineering across multiple squads.

You will shape model serving practices, drive scalable architectures, and mentor engineers while delivering high-impact, production-grade ML platforms in a hybrid work setting.