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Hello Fresh

Meal-kit delivery service with subscriptions

Staff Machine Learning Engineer - Adtech

AI Application Engineering +1Posted on Nov 11
Full-timeToronto, ON, Canada

About the Job

Requirements

  • Extensive hands-on experience in Machine Learning Engineering, AI Engineering, or Backend Software Engineering, with a strong track record of building and operating distributed production systems.
  • Production proficiency in Python and SQL, with strong fundamentals in software design, API development (such as FastAPI), testing, and database optimization.
  • Hands-on experience building and deploying production LLM and agentic applications beyond simple prototypes, including agent orchestration (LangGraph, LangChain, or similar), tool use, MCP, context management, and automated evaluations.
  • Experience with A/B testing or other experimentation methods, such as synthetic testing, to validate changes before and after launch.
  • Experience with multi-agent systems, such as supervisor and sub-agent designs, and the ability to explain the trade-offs between single-agent and multi-agent approaches.
  • Experience with multimodal AI models, including vision-language models for image or video analysis and generation.
  • Experience building systems where predictive machine learning models and LLM-based agents work together to drive automated decisions.
  • Familiarity with deploying and scaling classical machine learning or deep learning models in production, alongside solid MLOps practices including CI/CD, model monitoring, and automated pipelines.
  • Experience working with modern cloud and data platforms such as Databricks, Unity Catalog, MLflow, Docker, and AWS.
  • Proven experience operating at a Staff or Principal level, including systems thinking, navigating ambiguity, writing clear technical specifications, and mentoring other engineers.

Responsibilities

  • Design, build, and maintain production multi-agent AI systems and LLM workflows using modern orchestration frameworks such as LangChain and LangGraph.
  • Build the APIs, tool-calling interfaces, and MCP integrations that connect internal marketing automation services with AI agents and third-party platforms.
  • Partner with Data Scientists to move machine learning and statistical models from prototype into reliable production, and connect them to agents that create, launch, and optimize ads.
  • Design end-to-end system architectures across models, databases, and backend services, optimizing for reliability, correctness, and long-running workflows at scale.
  • Build evaluation pipelines, guardrails, state management, and human-in-the-loop approval workflows to ensure AI and ML systems run safely in production.
  • Author RFCs and Architecture Decision Records to align the team on technical direction and solve cross-team architectural challenges.
  • Raise the engineering bar across the team through code reviews, system design guidance, and active mentorship of Senior Engineers and Data Scientists.
  • Perform other duties as assigned.

Desired Qualifications

  • Prior background in AdTech, MarTech, e-commerce growth, or high-scale consumer platforms.

About the Company

Hello Fresh

Meal-kit delivery service with subscriptions

HelloFresh provides meal kits that deliver fresh, pre-portioned ingredients and chef-curated recipes to customers’ doors through a subscription service. Customers choose from a weekly menu of meals and dietary options (vegetarian, low-calorie, family-friendly), select their delivery day, and receive ingredients and step-by-step instructions to cook at home. The service eliminates meal planning and grocery shopping and can be flexibly managed—subscribers can skip weeks or cancel anytime. The company differentiates itself with a broad, customizable menu, focus on fresh produce and sustainable sourcing, and convenience for busy households. Its goal is to make home cooking easy, healthy, and accessible while providing flexible, affordable meal options.

Company size
10,001+
Company stage
IPO
Headquarters
Berlin, Germany
Founded
2011

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