IT B2C is the largest ecosystem in Sber. We are more than 8,000 people in 18 cities across Russia. We are engaged in the development and improvement of retail solutions, helping to make the Bank's services more accessible, safer, and more convenient.
We are waiting for you!
We are a team of experts united by a common passion for artificial intelligence and recommendation systems (RecSys).
Our main task is to create a modern, scalable recommendation platform capable of anticipating user expectations by offering personalized recommendations at every stage of their interaction with the Sber ecosystem. Our solutions cover a wide range of industries: from finance and e-commerce to entertainment and healthcare.
Our platform's development is built around the implementation of new SOTA models. We follow global trends, experiment with new approaches, implement them as part of the platform, and bring them to concrete business applications. We are looking for a Machine Learning Engineer to join the team to bring the Bank's recommendation models into PRODUCTION. We work with huge amounts of data and high-load services, which makes our work not only important but also technically interesting. The development of the recommendation platform product itself in the Bank also directly depends on us, as we determine its key growth points. If you are drawn to the idea of being a pioneer and want to be at the forefront of new technology, join us!
Responsibilities
- Development and improvement of End-to-End ML-pipelines
- Development of production data processing pipelines
- Work with huge volumes of Sber data (petabytes) on PySpark, research approaches to their application in models
- Write efficient and scalable code for training and inference of models on PyTorch, conduct experiments on a GPU cluster
- Performance optimization of code for processing large data arrays or high-load online recommendation services
- Mentoring junior team members, sharing knowledge and expertise.
Requirements
- Mathematical background
- Good knowledge of Python and key data manipulation frameworks (PySpark, PyArrow, Pandas)
- Experience in writing quality production code
- Experience in writing industrial data processing pipelines containing many steps, dependencies, and complex logic
- Experience using Airflow (or other industry-standard pipeline orchestrators such as Luigi, Dagster, etc.)
- Good understanding of SQL / NoSQL databases
- Skills in working with generative AI models; experience in creating AI agents and using them in work will be an advantage
- Experience using GigaChat, Kandinsky, and similar tools in products, skills.
Will be a plus:
- Experience with Kubernetes
- Experience with MLFlow (or other similar tools)
- Experience in distributed training of large models on a GPU cluster
- Experience or education in finance, banking
- Experience implementing online inference under high load conditions
- Experience optimizing data preprocessing pipelines for high load.
Tech Stack:
Python, PySpark, Airflow, Kubernetes, FastAPI, S3, PyTorch, MLFlow, Jira, Confluence, Git.
Conditions
- Comfortable modern office near Kutuzovskaya metro station (optional)
- Option to choose a convenient schedule – office/hybrid/remote (optional)
- Annual salary review, quarterly and annual bonuses (bonuses are indicated depending on the position and bonus system)
- Corporate gym and recreation areas
- Over 400 educational programs from SberUniversity for professional and career development
- Adaptation program and supervisor support at the start (for Junior level vacancies)
- Extended Voluntary Medical Insurance, preferential insurance for family and corporate pension program
- Flexible mortgage discount equal to 1/3 of the Central Bank's key rate
- Prime subscription with the possibility of shared use by three close people
- Reward for recommending friends to the Sber team.
