A hybrid work model requires 1 day a week in the office (Warsaw).
Machine Learning Research is Allegro’s R&D lab created to develop and apply state-of-the-art machine learning methods, helping Allegro grow and innovate with artificial intelligence. Beyond bringing AI to production, we are committed to advance the understanding of machine learning through open collaboration with the scientific community.
As a Machine Learning Engineer you will support Research Engineers in building Machine Learning models and then deploy them on production, ensuring high availability and performance.
Your work will be related to:
Designing, developing, optimizing and maintenance of data and ML training pipelines, including analysis, exploration and processing of text data
Collaborating with our Research Engineers, Software Engineers and Product teams across the model lifecycle
Production-running models management, monitoring and tuning
What we offer:
Support from experienced Machine Learning Engineers, Research Engineers, Data Engineers and Data Scientists
Well-located office (with fully equipped kitchens and bicycle parking facilities) and excellent working tools (height-adjustable desks, interactive conference rooms)
Annual bonus up to 10% of the annual salary gross (depending on your annual assessment and the company's results)
A wide selection of fringe benefits in a cafeteria plan – you choose what you like (e.g. medical, sports or lunch packages, insurance, purchase vouchers)
English classes that we pay for related to the specific nature of your job
Macbook Pro or Dell with Windows (if you don't like Macs) and other gadgets that you may need
Working in a team you can always count on — we have on board top-class specialists and experts in their areas of expertise
A high degree of autonomy in terms of organizing your team’s work; we encourage you to develop continuously and try out new things
Hackathons, team tourism, training budget and an internal educational platform, MindUp (including training courses on work organization, means of communications, motivation to work and various technologies and subject-matter issues)
If you want to learn more, check it out
We are looking for people who:
Have a bachelor or master's in machine learning, mathematics, computer science, statistics or related fields
Have proficiency in Python and experience with Kotlin/Java
Know ML models lifecycle
Have a practical experience in ML-based solutions development
Have at least basic knowledge of ML algorithms and common libraries used to deal with models (scikit-learn, PyTorch, Pandas)
Have Experience with natural language processing (NLP)
Have prior experience in running large-scale computation on cloud platform (GCP, AWS or Azure)
Have a practical experience in Python microservices and libraries development as well as maintenance
Can independently make decisions within a designated scope and take full responsibility for tasks taken, during their entire lifecycle: from requirements engineering, through implementation to deployment and maintenance
Know SQL
Know English on at least B2 level
The following are also a plus:
Docker
Practical knowledge of Kubernetes-based MLOps solutions (Kubeflow) and/or MLOps solutions available in the cloud (preferably Google Cloud Vertex AI)
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