Machine Learning Engineer
Role details
Job location
Tech stack
Job description
We are looking for machine learning engineers with passion for using machine learning to create intelligent applications. As a Machine Learning Engineer, you will be part of the Data Science team inside the Product and Tech group with +2200 brilliant developers, engineers, analysts and researchers. Honestly, it is a great environment!
You will work with brilliant people with world class knowledge, and a passion for sharing knowledge and solving interesting and complex problems. You will collaboratively design, build, and productionize machine learning systems. As a member of the logistics data science team, you will develop solutions to supply and demand challenges while optimizing logistics algorithms to ensure a reliable, efficient, and profitable service. From predicting ETAs to solving pricing problems, our ML pipelines form the backbone of our delivery operations.
- Please note that this position is located in Berlin (3 days a week from our Berlin office and 2 days working from home)
These are some of the key components to the role:
- Researching, implementing, and deploying innovative ML techniques applicable to logistics problems.
- Scaling and transferring novel machine learning solutions to improve our decision making processes and solutions in their different product use cases.
- Implementing and launching Data Science applications that coordinate with front facing solutions; constant improvements using performant and efficient ML models.
- Implementing tools, data pipelines, and evaluation frameworks to enable development, iteration, and launch of ML ideas within the product scope.
Requirements
Do you have experience in Python?, * Proven academic/industry experience in Machine Learning or similar, developing technologies for forecast or logistics products.
- Strong coding and software engineering skills in a mainstream programming language; Python preferred.
- Able to work independently and solve complex problems, with a demonstrated ability to productise and deploy using cloud services (eg. Amazon Web Services, Google Cloud Platform).
- Strong background in working with microservices and event-driven architecture, serverless computing, and cloud architecture patterns.
- Familiarity with tools and packages like Airflow/Mlflow/Docker/Kubernetes/Flink
- Ability to collaborate closely with multi-functional teams, driving forward best practices.