• Develop, deploy, and maintain machine learning models and systems
• Conduct in-depth data analysis, preparation, and processing
• Contribute to the design of ML systems, focusing on data-related aspects like annotation and processing
• Follow established security guidelines and identify potential risks
• Collaborate with peers to review and ensure designs are effective, flexible, and reusable
• Solve issues and provide feedback for engineering design and delivery processes
• Ensure that features are deployed according to requirements and operate without unintended side effects
• Create and maintain comprehensive documentation for models and processes
• Monitor and support deployed solutions to ensure they meet performance expectations
• Actively seek out information and internal solutions to ensure reusability and adoption of common technologies

• Help us challenge injustice by creating fair choices for millions of people across 47 countries.
• Develop your professional skills with access to mentoring, career consulting, and learning programs.
• Collaborate with teams around the world and gain international experience through our Global Talent Exchange Program.
• Engage in company-wide challenges, awards, sports activities, employee-led social impact and volunteering projects.
• Work alongside people who take initiative, speak openly, and challenge themselves to grow.
• Improve your language skills through co-financed courses and internal speaking clubs.

• 3+ years of experience in the Field. Previous software engineering experience is preferrable
• Strong proficiency in Python and its common data processing frameworks (e.g., for streaming, batch, and asynchronous data).
• Strong foundational knowledge in classical machine learning, deep learning, and advanced mathematics.
• Familiarity with MLOps principles and experience using MLOps tools for managing the ML model lifecycle.
• Experience with backend systems and infrastructure, and a willingness to learn new technologies (like Golang) for integration purposes.
• The ability to design solutions using common design patterns and various design tools.
• Experience in experiment design and the ability to validate the credibility of information from various sources.
• A solid understanding of the business value of delivered features.
• Strong problem-solving skills, with an emphasis on using technical facts and reasoning.
• Excellent communication and collaboration skills, including active listening and the ability to build consensus.
• A strong drive for continuous self-development and a collaborative mindset for contributing to communities.
• Ability to balance quality and speed depending on the case, and justify your decision
• Ability to explain the background and consequences of your technical decisions to non-technical stakeholders, including product and business managers
• English communication skills

Desired Candidate Profile

• 3+ years of experience in the Field. Previous software engineering experience is preferrable
• Strong proficiency in Python and its common data processing frameworks (e.g., for streaming, batch, and asynchronous data).
• Strong foundational knowledge in classical machine learning, deep learning, and advanced mathematics.
• Familiarity with MLOps principles and experience using MLOps tools for managing the ML model lifecycle.
• Experience with backend systems and infrastructure, and a willingness to learn new technologies (like Golang) for integration purposes.
• The ability to design solutions using common design patterns and various design tools.
• Experience in experiment design and the ability to validate the credibility of information from various sources.
• A solid understanding of the business value of delivered features.
• Strong problem-solving skills, with an emphasis on using technical facts and reasoning.
• Excellent communication and collaboration skills, including active listening and the ability to build consensus.
• A strong drive for continuous self-development and a collaborative mindset for contributing to communities.
• Ability to balance quality and speed depending on the case, and justify your decision
• Ability to explain the background and consequences of your technical decisions to non-technical stakeholders, including product and business managers
• English communication skills