EcoVadis is hiring a
Senior Machine Learning Engineer

Summary

Join our growing AI Center of Excellence as a highly motivated Machine Learning Engineer to drive innovation across the organization.

Requirements

• M.S. Degree in Computer Science, Mathematics, Engineering, or a related technical discipline
• Demonstrable experience in designing ML systems and integrating those into business applications, and model monitoring at scale
• Excellent knowledge of Python, other languages are a plus
• Experience with web crawling and related frameworks like scrapy, beautifulsoup, lxml, etc. is a plus
• Deep knowledge of machine learning lifecycle, principles, and tooling
• Experience in cloud technology, preferably Azure and its ecosystem (e.g., Azure Data Factory, Azure Bicep, AzureML and Azure Cloud Storage) and Databricks
• Solid understanding of software engineering best practices across the development lifecycle, including agile methodologies, coding standards, code reviews, source management, build processes, testing, and operations
• Experience in Linux OS and scripting
• Knowledge of data management fundamentals and data storage principles

Responsibilities

• Leverage data to solve business problems of various business units at EcoVadis
• Design, develop and maintain scalable ML systems
• Run large-scale experiments and tests to ensure quality and efficiency of ml- and data pipelines
• Ensure ML lifecycle monitoring by applying the best practices in MLOps and green IT
• Support large-scale data preparation and prelabelling
• Engage in building ML engineering infrastructure and systems to orchestrate batch and streaming pipelines, and leverage Infrastructure as Code (IaC) to manage and provision infrastructure
• Partner with scientists and engineers to make ML models accessible to end-users and downstream processes
• Build and enhance feature stores

Benefits

• International and very friendly work environment
• Hybrid for colleagues who live near our offices (4 days per month) otherwise remote in the country you were hired in
• Flexible working hours
• Home office allowance program
• IT equipment allowance
• 90 days Working from anywhere policy
• Meals and Transportation Vouchers (Cobee card)
• Health, Wellness, and Dental Benefits
• Unlimited and completely confidential sessions with psychologists and online well-being programs
• Employee referral bonus policy
• Paid employee volunteer day
• Paid moving day ( 1/year)
• Access to online learning library
• Virtual and in-person team building events