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HelloFresh

Staff Machine Learning Engineer, AdTech

HelloFresh is hiring a Staff Machine Learning Engineer, AdTech in Toronto, Canada. Level rates it ; you can apply on HelloFresh.

Level's read on this job

  • Level rates this machine learning engineer role Builds AI ●●●●: the job is building AI systems.
  • Tools and skills the posting cites include LangChain, LangGraph and MLflow.
  • Level classifies it as a lead role.
  • Level lists 373 other open roles at HelloFresh.
  • The role was posted in the last few days.

AI in this role

LangChainLangGraphMLflowDatabricks
ai-agentsml-ops

S'more about the team

The Growth Tech Alliance's goal is to enable HelloFresh to drive smarter marketing through AI-powered automation, intelligent systems, and advanced optimization. We've pioneered cutting-edge approaches to programmatic campaign optimization using state-of-the-art AI/ML technologies and are now scaling these capabilities across our global marketing operations. Our team’s key responsibilities include designing, building, maintaining autonomous decision-making systems, advanced ML models for marketing optimization, and real-time intelligence that drives measurable business impact. We work at the intersection of modern AI architectures, agentic systems, and enterprise-scale ML operations.

We are looking for a Staff Machine Learning & AI Engineer to lead the engineering architecture of our multi-agent AI systems, with a focus on agent-driven products. In this role, you will bridge the gap between our data scientists and backend engineers, connecting internal automation APIs, AI agents, and the supporting ML models into a cohesive production platform.

At HelloFresh, Staff Engineers are hands-on builders. You will spend roughly 50% of your time writing production code and building critical systems, and 50% driving technical direction through architecture reviews, RFCs, and mentoring engineers and data scientists across the team.

Lettuce share what this role will be responsible for

  • 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 our internal marketing automation services with AI agents and third-party platforms.
  • Partner with Data Scientists to take machine learning and statistical models from prototype into reliable production, and connect them to the agents that create, launch, and optimize ads.
  • Design end-to-end system architectures across our 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 (ADRs) 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.
  • All other duties, as assigned

Sound a-peeling? Here's what we're looking for

  • 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 (e.g., 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 (e.g., synthetic testing) to validate changes before and after launch.
  • Experience with multi-agent systems (e.g., 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 (vision-language models for image or video analysis and generation).
  • Experience building systems where predictive ML models and LLM-based agents work together to drive automated decisions.
  • Prior background in AdTech, MarTech, e-commerce growth, or high-scale consumer platforms.
  • Familiarity with deploying and scaling classical machine learning or deep learning models in production, alongside solid MLOps practices (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—able to think in systems, navigate ambiguity, write clear technical specs, and mentor other engineers.

Let’s cut to the cheese, this is why you'll love it here

  • Box Discount - Amazing discounts on 1 box per week! 75% discount on weekly HelloFresh and Chefs Plate meal kits AND 50% off weekly Factor meal box.
  • Health & Wellness - Health & Dental benefits from day 1, a Health Spending Account, unlimited access to the Headspace app to meet your self-care needs, and 25% discount on GoodLife fitness memberships!
  • RRSP Matching - Competitive RRSP matching program offered
  • Vacation & PTO - Time off is also an important part of self-care! We offer generous vacation and PTO to help you create a good work-life balance.
  • Family Benefits - A parental leave top-up program for expectant parents.
  • Growth & Development - We support your career progression and invest in your continued learning through experiences and initiatives owned by our dedicated L&D team
  • Work Hard & Have Fun - From team socials to engaging company days, you’ll have plenty of opportunity to experience the fun!
  • Diversity & Inclusion Initiatives - With impactful ERG’s like FreshPride, Women Empowered and LIMES, we are committed to our diversity, equity & inclusion efforts.
  • Food Puns - this one is kind of a big dill if you haven’t already noticed. We even have some punny meeting room names!

Flexible Hybrid Approach

At HelloFresh, we know that flexible work arrangements are essential in enabling you to do your best work, while balancing your personal and life needs. Offering remote work flexibility, along with the opportunity to interact and collaborate in the office are all a part of creating a great employee experience.

To meet these needs, we are pleased to provide Flexible Hybrid work. Flexible Hybrid is a people-first approach that is based on choice, trust, personalization, and empowers teams to choose when and how often they work from the office and work from home, in addition to team days and company days. This means a minimum of 2 days in office per week, with most teams in office between 2-3 days a week.

#LI-HYBRID

HelloFresh Canada uses AI-integrated technology to help us process and evaluate applications more efficiently. This includes tools that screen and assess candidate qualifications based on the requirements for this role. While these tools assist our workflow, all final selection decisions are made by our hiring team.

This is a posting for an existing vacancy. We are actively seeking to fill this position.

How we rate this

Staff Machine Learning Engineer, AdTech at HelloFresh rates 99 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

AI agentsML OpsLangChainLangGraphMLflowDatabricks

Also common in machine learning engineer jobs at this level

Pay

Typical machine learning engineer job
$197k

This posting doesn't list pay. Use the typical machine learning engineer figure when pay comes up. See machine learning engineer jobs

Questions you could be asked

  1. How do you decide when an AI agent can act on its own versus asking for approval first?
  2. How do you monitor a model once it's live, and how do you know it needs retraining?
  3. What are the limits of LangChain that you've run into, and how did you work around them?
  4. What's a project where you used LangGraph hands-on?
  5. Walk me through how you've used MLflow in your day-to-day work.

Adapt your resume

  • List these exact terms on your resume: AI agents, ML Ops, LangChain, LangGraph, and MLflow. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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