Research Engineer - Machine Learning

  • Pełny etat

Opis stanowiska

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.

Why is it worth working with us?

  • Being a part of Machine Learning Research team, you will be responsible for bringing to production research solutions for Allegro

  • While working on a new problem, you will explore it in depth and conduct literature review, looking for the most promising techniques for a given problem

  • You will be responsible for the preparation of the production-grade machine learning models, supporting the development team for a correctly functioning production model and meeting technical and performance requirements

  • You will support other teams in the implementation of tasks requiring the use of ML models. Your support will be needed both at the technical (e.g., what architecture will be appropriate for the domain) and best-practices level (e.g., building data sets, modeling, metrics, implementation of the ML-based solutions to the production) 

  • To apply state-of-the-art solutions, you will stay up to date with the scientific progress. You will deepen your knowledge by reading the latest papers in your domain, sharing the knowledge with other team members of the research teams operating in Allegro. You will participate in scientific conferences, visiting venues where the latest discoveries are presented

  • You will have the possibility to share the results of your research in the scientific community, and by taking part in the scientific conferences (oral presentations, poster sessions). You will develop your scientific career, as well as Allegro's presence in the science community

  • In your daily work you will expand your knowledge by cooperating with people who have hands-on experience in implementation of the ML models at scale unprecedented anywhere else in Poland

What we offer:

  • A hybrid work model that you will agree on with your leader and the team. We have well-located offices (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

  • 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 master's or PhD in machine learning, mathematics, computer science, statistics or related fields

  • Have a good knowledge of machine learning techniques (neural networks, random forests, Bayesian models) in at least one domain (information retrieval, natural language processing and understanding, time series, etc.)

  • Know the methodology of conducting scientific research and the use of iterative process of conducting experiments

  • Have experience in working with real data that deviate from the standard, well-developed collections used in research

  • Use statistical methods

  • Know Python and libraries necessary to work with model development (PyTorch, Tensorflow, XGBoost, LightGBM, Pandas, Numpy, etc.) 

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