at Allegro
Full TimeA hybrid work model requires 1 day a week in the office (Warsaw).
At MLR Computer Vision, our primary objective is to elevate the user experience by leveraging machine learning image processing algorithms. We specifically concentrate on image representation learning for Visual Search, the development of robust image classification, object detection and image captioning models. Our image representation models are also used for visual recommendations, product matching and improvement of product catalogue’s quality.
Why should you work at Allegro?
Being a part of the Machine Learning Research team, you will be responsible for finding solutions to computer vision research problems that we encounter at Allegro (e.g. visual search, image classification, object detection, multimodal representation learning)
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. You will be responsible for a correctly functioning production model and meeting desired latency requirements
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 world of science
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:
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)
Long term discretionary incentive plan based on Allegro.eu shares
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)
Participation in top-tier ML/AI international conferences
Internal ML-seminars (both covering broad ML topic, as well as domain oriented)
We are looking for candidates who:
Write clean, robust, and production-grade code in Python and showcase proficiency in ML frameworks, particularly PyTorch, PyTorch Lightning, and Transformers
Exhibit a track record of shipping and maintaining ML models in production environments
Have a good knowledge of machine learning techniques in the field of computer vision (convolutional neural networks, vision transformers, image classification, detection and segmentation models, representation learning, multimodal learning, diffusion models)
Have experience in working with real data that deviate from the standard, well-developed collections used in research
Possess a solid understanding of scientific research methodologies. Are proficient in the iterative process of conducting experiments, including hypothesis formulation, experimental design, data collection, analysis, and result interpretation
Have experience with training machine learning models in a distributed cloud environment will be an advantage (e.g. Google Cloud Platform, Kubernetes)
The following are also a plus:
Experience with machine learning in other domains (e.g. information retrieval, natural language processing and understanding, reinforcement learning, recommender systems)
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