• Analyze and validate product team hypotheses by interpreting large-scale datasets, identifying trends, and generating actionable insights. • Conduct external data analysis by integrating and assessing new data sources, replacing legacy sources, and ensuring compliance with terms of service. • Extract and create features from datasets for machine learning models, driving predictive capabilities and enhancing product performance. • Collaborate with cross-functional teams to translate data-driven insights into business recommendations that guide product decisions and strategy. • Apply advanced statistical analysis, linear algebra, and modeling techniques to large datasets, providing deep, actionable insights. • Perform large-scale data analysis comfortably using tools such as Python and SQL. • Work with Azure Machine Learning for model training and deployment, leveraging cloud capabilities to enhance data analysis workflows. • Participate in Scrum agile development process, focusing on iterative improvements through data-informed decision-making.

• Design, implement, and improve machine learning Systems. • Contribute to machine learning applications end to end, i.e. from research to prototype to production. • Work with product managers, designers, and customers to define vision and strategy for a given product.

Role Description As a Senior ML Data Engineer, you will help shape and expand the pipelines that power our products and research efforts. You’ll work across teams to design, maintain, and improve high-performance data pipelines, ensuring that data is accessible, reliable, and scalable to meet the needs of our users and internal stakeholders. - Maintain, extend, and improve existing data/ML workflows, and implement new ones to handle high-velocity data. - Provide interfaces and systems that enable ML engineers and researchers to build datasets on demand. - Influence data storage and processing strategies. - Collaborate with the ML team, as well as frontend and backend teams, to build out our data platform. - Reduce time-to-deployment for dashboards and ML models. - Establish best practices and develop pipelines and software that enable ML engineers and researchers to efficiently build and use datasets. - Work with large datasets under performance constraints comparable to those at the largest companies. - Iterate quickly, with a focus on shipping early and often, ensuring that new products or features can be deployed to millions of users. Qualifications - Minimum of 3 years of experience in a data role involving designing and building data stores, feature engineering, and building reliable data pipelines that handle high loads. - At least 2 years of professional software development experience in a role other than data engineering. - Proficiency in Python and experience working with Kafka infrastructure and distributed data systems. - Deep understanding of SQL and NoSQL databases (preferably Clickhouse). - Familiarity with public cloud providers (AWS or Azure). - Experience with CI/CD and orchestration platforms: Kubernetes, containerization, and microservice design. - Proven ability to make independent decisions regarding data processing strategy and architecture. - Thoughtful, self-directed individual who is able to operate effectively in a fast-paced environment. Requirements - Experience collaborating across ML, backend, and frontend teams. - Understanding of machine learning fundamentals, including model training, inference, and frameworks such as PyTorch or TensorFlow. Benefits - Fully remote position with flexible working hours. - An inspiring team of colleagues spread all over the world. - Pleasant, modern development and deployment workflows: ship early, ship often. - High impact: lots of users, happy customers, high growth, and cutting-edge R&D. - Flat organization, direct interaction with customer teams. - We celebrate equality of opportunity and are committed to creating an inclusive environment for all team members.

• Explore and apply advanced machine learning techniques, including large language models (LLMs) and deep learning, to solve complex challenges across the organization. • Collaborate with management and stakeholders to define strategic roadmaps and translate them into actionable quarterly plans. • Drive project execution by managing priorities, deadlines, and deliverables, leveraging your technical expertise. • Develop high-quality, well-tested Python code to address critical product and business needs. • Design scalable, secure systems using state-of-the-art AI/ML technologies and industry best practices. • Mentor team members on design principles, coding standards, and the adoption of AI productivity tools. • Troubleshoot and resolve complex technical issues to improve system reliability, scalability, and operational efficiency. • Ensure the security, scalability, and architectural integrity of feature designs through thorough reviews across teams.