Senior Machine Learning Engineer
Role details
Job location
Tech stack
Job description
We are looking for senior machine learning engineers (but open for mid level) 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 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. What will you bring to the team?
Requirements
- Strong experience in Machine Learning, developing real time data streaming technologies for forecast or logistics products
- Strong coding and software engineering skills in Python
- 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 including Kubernetes (deployment in production) is expected. A plus would be additional experience with Airflow/Mlflow/Docker/Flink
- Experience collaborating closely with multi-functional teams, driving forward best practices.
- Real-time production systems at scale (millions of requests/transactions per day) is expected for senior level, but would be a non-mandatory plus for mid level