Machine Learning Engineer, Retail Platforms

We’re seeking a Machine Learning Engineer to join our cross-functional Inventory Routing and Replenishment team within the Merchant Group. The team works on developing tooling for our Merchants and Wolt Markets to efficiently manage their inventory. The ML Engineer will also work with other teams in the Retail Platforms domain. In close collaboration with applied scientists, software engineers, product managers, designers, and analysts, the MLE will be helping the teams to develop and maintain applied science solutions in production, solving problems like demand forecasting, inventory optimisation, and pricing, empowering our Merchants to automate and optimise their retail inventory and pricing processes. 

What you’ll be doing

As a Machine Learning Engineer in Wolt’s Retail Platforms domain you will:

• Develop, train, deploy and maintain ML solutions that solve business challenges in the retail domain. Monitor, maintain and troubleshoot the domains ML solutions;
• Improve performance, robustness and observability of the domain’s ML solutions, and generally raise the team’s ML engineering excellence bar;
• Contribute to our MLOps practices and liaise with Wolt’s ML Platform team and wider MLE community.
• Work at Wolt’s scale: Wolt operates in 31 different markets with as many languages and millions of customers;
• Be part of a cross-disciplinary team with Applied Scientists, Software Engineers and Analysts to provide solutions to customer problems with a direct impact on the company’s business KPIs;

Our humble expectations

• You are experienced in end-to-end machine learning deployments and maintenance of ML systems and have at least 2+ years of experience in ML/MLOps;
• You have deployed and ran ML models in production at scale;
• You bring solid experience in scaling and troubleshooting machine learning deployments to the table. You can help with the technical issues the teams encounter;
• You are experienced in supporting ML solutions with high data processing & distributed computing needs (and familiar with suitable frameworks like Spark & Ray), and familiar with real-time ML inference;
• Experience in the retail or similar industries is highly preferable;
• Good understanding of ML and MLOps principles as well as Software engineering experience in Python should complete your profile;
• Experienced in Docker, Kubernetes, workflow orchestration tools (e.g. Flyte), model and experiment registries (e.g. MLflow) and model serving systems (e.g. Seldon);
• You have solid communication and collaboration skills and are experienced in coordinating initiatives with your team and main stakeholders.