As a Senior Machine Learning Engineer, you will develop and improve real-time recommendation engines for our Sponsored Product advertisements.
Every time a customer searches for products on Zalando, a bidding request is sent to our system in real-time. The system aggregates relevant data points, evaluates thousands of candidate ads with our machine learning models, and decides what product to show where – all within 10-30 ms. Every second, we score tens of thousands of ad contents
In this position, you will design, develop and run the recommendation engine that optimizes this process – with emphasis on high-load low-latency Engineering techniques. The technical scope covers high-availability engineering, concurrent programming, network programming, distributed systems, distributed databases, large-scale data processing, stream processing, algorithm performance and system performance optimization.
WHERE YOUR EXPERTISE IS NEEDED
Improve our real-time recommender system, especially on engineering aspects (resource efficiency, reliability, maintainability etc.)
Improve the pace of innovation and experimentation by building and improving ML infrastructures
Collaborate with brilliant Product Managers, Data Scientists, Engineers and Analysts across Zalando to create positive customer impact together
Help define our team’s objective. Continuously improve the self-organization of the team.
Mentor and grow junior and mid-level members of the team
3+ years of experience in a) high-load real-time API development or b) large-scale data processing. Experience in developing and running ML enabled systems is highly preferred
Expertize in either a) concurrent programming (multi-threading), network programming, distributed databases, microservice architecture, or b) large-scale data processing (experience in stream processing is preferred)
Good grasp in basic statistical concepts and Machine Learning (experience in modelling is NOT required)
Experience in some of the following technologies Java, Python, Scala, C++, Go, Flink, Storm, Spark Streaming, Kafka, Spark, Presto, Redshift, Vertica, Airflow, numpy, scipy, scikit-learn
Outcome-obsessed, pragmatic engineer who is relentlessly focused on creating positive customer impact
Level of experience (years):
Mid Career (2+ years of experience)