Research Engineer

Research Engineer - Machine Learning

Opening soon:)

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.

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

• 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 development of the large-scale machine learning solutions from building datasets, through experimentation, to delivering production-ready models,

• supporting the engineering team in launching machine learning models to production, assuring it meets 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 MLs-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 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.

• Flexible working hours in an office first model (4/1).

• 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 (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

• 16" or 14" MacBook Pro and other gadgets that you may need

• 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

• Graduated with a degree strongly related to statistical/mathematical modeling, such as Mathematics, Computer Science, Data Science, Physics, Economics or similar major. 

• Possess strong ML knowledge in at least one domain (e.g. natural language processing, information retrieval, computer vision, tabular data), keeps track of state-of-the-art.

• Want to translate business challenges into ML problems and conduct research, including choosing impactful research problems and  carrying out long-running projects.

• Program in Python and efficiently use basic development tools. 

• Have hands-on experience with ML frameworks (e.g. PyTorch, Tensorflow) and large-scale applications of ML.

• Have experience in data analysis using SQL and building data processing pipelines.

• Can clearly communicate with business units - from formulating an analytical problem to a clear and intuitive presentation of results.

This may also be of interest to you: 

Allegro Tech Podcast →

Send in your CV and see why it is #dobrzetubyć (#goodtobehere)