Machine Learning Platform Engineer at Picnic

✨ The Stack take on this Picnic job

• 🏢 Picnic has 8 jobs here, 4 with visa sponsorship see all
• 🛂 Visa: Highly Skilled Migrant permit. 30 or older: €5,942 a month guide
• 💡 No salary shown. Ask Picnic if it is above the minimum.

Updated 2026-11-05, from our data and official sources.

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About the role

IN A NUTSHELL  You’ll work on building the tools and infrastructure to help our Machine Learning Engineers build and productionize robust machine learning models.  Working closely with ML Engineers, you’ll identify opportunities to improve the machine learning lifecycle at Picnic. From tools that improve model experimentation, to automations that simplify model deployment. You will collaborate with other platform teams at Picnic to make sure our tech stack remains aligned with the rest of the Tech team, while building and integrating the solutions that solve the problems unique to machine learning systems. Check out some of our previous machine learning projects here: https://blog.picnic.nl/tagged/machine-learning [https://blog.picnic.nl/tagged/machine-learning] TRICKS OF THE TRADE Various MLOps-oriented projects to: * Automate machine learning model retraining and deployment * Set up infrastructure for model inference, supporting deployment strategies like A/B testing and shadow testing. * Improve monitoring in order to exercise operational excellence for our machine learning models. * Simplify and standardize usage of tools by developing and inner-sourcing Python packages Your contributions to the platform will power: * Recommendation systems: generating useful in-app grocery suggestions for our customers. * LLM use cases: automate systems to categorize Customer Success tickets. * Outlier detection: low-latency decision support systems to prevent fraud. * Time-series forecasting: improving the operational efficiency throughout our supply chain, from our automated fulfilment center to our last mile delivery. You will definitely:  * Work closely with ML Engineers to identify opportunities, define use cases, and scope out the requirements * Own your own project from inception to production * Experiment with and evaluate new technologies * Develop tools and enhancements for our ML Platform WE’RE LOOKING FOR * Master’s degree or higher in AI, Computer Science, or a related field * 3+ years of experience in productionizing machine learning models, preferably using Docker and Kubernetes * Proven track record of scaling development and adoption of machine learning in an organization * Strong Python skills * ...and for bonus points: you have experience with Terraform and working with LLMs PICNIC PERKS ✍🏼 Every expert was once a beginner!  You’ll get plenty of opportunities to challenge yourself and grow, including the Picnic Tech Academy, Lunch & Learn sessions, and tailored soft skills training. We also offer free professional weekly language courses. 🫱🏼‍🫲🏾 Teamwork makes the dream work With more than 80 nationalities across 3 countries, you’ll be part of a diverse company with plenty of cool stuff to get involved with, from board game evenings to after-work drinks to our company ski trip and more! 🥗 Fresh Lunch, coffee, and snacks  Our offices are equipped with fully-fledged coffee bars and a kitchen and chefs. They prepare delicious fresh and warm lunches every day so you can keep your energy up. 🚲 Health insurance discount & bike plan We have a partnership with CZ (a health insurance provider). Picnic employees get a discount on CZ insurance packages between 5% and 15%. Furthermore, through our partnership with Lease a Bike, you can rent-to-own a new (e)bike at a discounted rate 🌎 Relocation If you’re moving from another country to join Picnic we make it as smooth as possible for you. We’ll cover your flight costs for you and your partner and kids, your first month's rent and moving costs (up to €2000), and help you with the 30% tax ruling setup and application.  📆 All the rest  At Picnic you get 25 holidays, we cover your travel expenses and offer a pension plan. And your phone and laptop are on us, as well.

Responsibilities

You will work on building tools and infrastructure to support Machine Learning Engineers in productionizing machine learning models. This includes identifying opportunities to improve the machine learning lifecycle and collaborating with other platform teams.

Requirements

A Master's degree or higher in AI, Computer Science, or a related field is required, along with 3+ years of experience in productionizing machine learning models. Strong Python skills and experience with Docker and Kubernetes are essential.

Key skills

• Machine Learning — in 291 other listings on The Stack
• MLOps — in 72 other listings on The Stack
• Python — in 1200 other listings on The Stack
• Docker — in 464 other listings on The Stack
• Kubernetes — in 585 other listings on The Stack
• Terraform — in 470 other listings on The Stack
• LLMs — in 80 other listings on The Stack
• Model Deployment — in 10 other listings on The Stack
• Model Inference
• Monitoring
• Operational Excellence
• Time-Series Forecasting
• Outlier Detection
• Recommendation Systems
• Automation
• Collaboration
• Experimentation

On The Stack (as of 2026-11-05)

• Picnic has 7 other listings on The Stack, 4 of them marked as offering visa sponsorship.
• 66 visa-sponsored jobs in the Netherlands on The Stack, posted in the last 60 days.
• 29 visa-sponsored machine learning engineer jobs in Europe on The Stack, posted in the last 60 days.

Frequently asked questions about Picnic

Is this Machine Learning Platform Engineer role still open?

It was posted on 2026-03-17. Listings more than 30 days old may no longer be active, so check Picnic's site before applying.

Where is this role located?

The listing gives the location as Amsterdam, North Holland, Netherlands.

What skills does this role ask for?

The listing names: Machine Learning, MLOps, Python, Docker, Kubernetes, Terraform, LLMs, Model Deployment, Model Inference, Monitoring, Operational Excellence, Time-Series Forecasting, Outlier Detection, Recommendation Systems, Automation, Collaboration, Experimentation.