Senior Machine Learning Engineer, Trustpair

• Strong experience as a Machine Learning Engineer, ideally in a SaaS or product-driven environment
• Proven experience delivering AI/ML projects into production
• Strong Python expertise and the ability to build robust, scalable, production-ready systems
• Solid fundamentals in statistics, probability, algorithms, data structures, and model evaluation
• Experience with both traditional ML approaches and LLM / GenAI frameworks
• Experience building or contributing to evaluation workflows for ML or LLM-powered features, including datasets, validation metrics, and quality monitoring
• Strong software engineering practices: version control, testing, linting, CI/CD, DevOps workflows, and production monitoring
• Ability to connect technical decisions to business value and customer impact
• Excellent communication skills and the ability to collaborate with Product, Data, and Engineering stakeholders
• Fluent English (French is a plus)
• Nice-to-Haves

Desirable

• We are looking for a Senior Machine Learning Engineer to join our Core Evaluation team and help spearhead Trustpair’s next phase of AI and ML development
• In this role, you will work at the intersection of predictive machine learning, software engineering, fraud prevention, and generative AI
• Your mission will be to strengthen Trustpair’s fraud prevention engine by designing, training, evaluating, and deploying robust ML models using our proprietary data, while also contributing to the evolution of our agentic Risk Engine
• You will play a key role in transforming advanced fraud detection techniques into scalable product capabilities, helping Trustpair remain at the forefront of vendor fraud prevention
• This is a high-impact role for someone who is both deeply technical and business-oriented, someone who knows when ML is the right answer, when it is not, and how to measure the real-world impact of what they build
• Design, build, and optimize core ML capabilities, with a strong focus on name matching, risk scoring, fraud detection, and decision-support systems
• Contribute to the architecture and scaling of Trustpair’s agentic Risk Engine, ensuring it delivers clear, actionable risk insights to end users
• Build, train, evaluate, and monitor both traditional ML models and LLM-powered features in a production SaaS environment
• Create robust evaluation frameworks for AI/ML initiatives, including golden datasets, validation metrics, quality benchmarks, and measurement processes
• Partner closely with Product, Engineering, and Data stakeholders to define the technical roadmap for AI initiatives
• Help evaluate the business value of AI/ML features by measuring performance, cost, reliability, token consumption, and customer impact
• Bring strong software engineering practices to ML development, including clean Python code, version control, testing, CI/CD, observability, and scalable deployment practices
• Stay close to emerging AI/ML practices and identify where they can be applied pragmatically to improve Trustpair’s fraud prevention capabilities

• First call with Céline (Chief People Officer) - 30 mins, video call
• Technical case study with Benoit Patra (Chief Technology Officer) & Lucas Simonet (ML Engineer) - 1 hour, remote
• Coffee fit with team members - 30 minutes, onsite
• Cofounders interview with Alexandre Gillier - 45 minutes, onsite