Senior Machine Learning Engineer

About You 

Are you looking for an opportunity to work on meaningful, cutting-edge projects in machine learning? Did you wonder what it would be like to work at a company where your contribution has a real, measurable impact – and you are rewarded for it?    

If you have a knack for data and aspire to contribute to ethical tech development, then this is the perfect role for you! We value autonomy, allowing you to bring your innovative ideas to fruition in an inclusive, feedback-oriented environment. Your work on NLP will directly contribute to advancing corporate responsibility through technology.

Your Responsibilities 

As a Senior Machine Learning Engineer, you will play a key role in developing and scaling state-of-the-art Machine Learning applications within RepRisk’s Entities team. You will contribute to the design, implementation and operation of Machine Learning products as part of our global Data division. Moreover, you will: 

• Design and implement advanced ML features that deliver measurable business value, taking end-to-end ownership from ideation to production 

• Architect, build, and optimize multifaceted search engines that aggregate and retrieve data across multiple sources (meta-search) 

• Design scalable system architectures and drive technical decisions to ensure long-term maintainability and performance 

• Develop, integrate, and maintain microservices within larger applications 

• Apply and guide the adoption of the latest advancements in ML, including Large Language Models (LLMs) 

• Train, evaluate, and optimize models for performance, scalability, and reliability in production environments 

• Mentor and support other ML engineers, fostering a collaborative and high-performing engineering culture 

• Collaborate closely with ML engineers, backend engineers and product owners, to continuously improve the products, acting as a technical lead in cross-functional initiatives 

• Contribute to a well-balanced tech stack, prioritizing both simplicity, maintanability and efficiency  

• Ensure high-quality, maintainable code through thorough code reviews and engineering best practices  

• Actively contribute in Agile/Scrum processes, sharing insights and feedback