• Work with colleagues from our Data & AI department and collaborate closely with product managers, engineers, and commercial stakeholders. • Design, build, and operate machine learning systems for retail media use cases such as sponsored product ranking, audience segmentation, campaign optimization, attribution, and performance measurement. • Translate business and product requirements into scalable ML solutions, balancing model quality, latency, reliability, scalability, and maintainability. • Develop robust ML pipelines for feature engineering, training, evaluation, deployment, monitoring, and continuous model improvement. • Bring models into production using our cloud-based stack and ensure they are reliable, observable, and maintainable over time. • Communicate technical decisions, assumptions, limitations, and uncertainty clearly to product, engineering, and business stakeholders. • Contribute to ML engineering standards, best practices, and knowledge sharing within the team.

• Represent Informatics as the subject matter expert in cross-functional initiatives, collaborating closely with scientists, data teams and technical stakeholders. • Identify and deliver solutions where computational and AI-driven approaches can improve efficiency, decision making, and knowledge utilization across complex workflows. • Design, develop and deploy production-ready AI-enabled applications that integrate structured and unstructured data for scalable use. • Build and implement retrieval-augmented systems (RAG), semantic search frameworks, and embedding-based data workflows to enable intelligent information retrieval. • Develop conversational AI interfaces and agentic architectures that automate knowledge-intensive tasks and support user interaction with complex datasets. • Research, design and develop machine learning algorithms, tools and systems; • Write structured, tested, readable and maintainable code, as well as participate in code reviews to ensure code quality and distribute knowledge for existing and new tools that allow us to extract, analyze, visualize and produce valued added insights of data for our customers; • Independently pursue research projects, implement novel computational workflows and support a dynamic multi-disciplinary team focused on moving projects from the hit identification stage to the discovery of candidates; • Perform statistical analysis and fin-tuning using test results; • Be actively involved in digital transformation projects and data governance objectives working closely with IT and scientific teams; • Ensure proper documentation of workflows, reports, tools, and analyses of the Research Informatics’ team; • Provide transparency and regular communication on project status, potential roadblocks for execution, and new strategies with Technology Sciences leadership; • Ensure project activities align with Eurofins policies, procedures and methodologies

• Design, construct, and manage robust data pipelines for the training, validation, and continuous retraining of Large Quantitative Models (LQMs) and agentic frameworks • Develop, implement, and rigorously test novel ML models and algorithms, defining appropriate metrics to ensure model performance aligns with high-level product objectives • Lead the effort in cleaning, transforming, and engineering features from complex and large-scale datasets to optimize LQM performance and predictive accuracy • Conduct deep analysis of model behavior, performance, and failure modes, tuning hyper-parameters and optimizing model architecture for efficiency, speed, and accuracy in a production context • Collaborate closely with AI researchers, product managers, and SWEs to translate high-level business objectives into actionable ML development and deployment roadmaps • Champion and enforce exceptional engineering standards for code quality, system efficiency, and security in a prototyping environment • Drive technical execution with high autonomy, making critical design and implementation decisions independently

Role Description The ML engineer role is responsible for designing, building, deploying, scaling, and evolving the end-to-end AI & ML systems at Revionics (demand modeling and forecasting, optimization, product relationships, generative and agentic capabilities, etc.) Qualifications - In the process of obtaining a Bachelor's/Master’s degree in computer science or related STEM field, or have equivalent work experience - Experience with software development (backend/frontend, data engineering, APIs, system design, etc), Python, SQL and/or Scala - Familiarity with using LLM-based AI platforms such as Claude or Codex - Research or practical project experience in AI or ML, such as agentic systems (e.g. coding agents, multi-agent orchestration, skills/plugins frameworks), predictive modeling, LLMs, reinforcement learning, or others - Interest in applying AI, ML, and data science/statistical techniques within software products and production systems, demonstrated through projects - Strong algorithmic problem-solving skills and an analytical mindset - Enjoy tough technical challenges and are naturally intellectually curious - Seek to drive change and influence others through clear and effective communication Requirements - Build out new features on our Science roadmap: agentic AI as we introduce code-on-the-fly capabilities, product relationships, and rules management; predictive and decision-making AI as we build out our unified forecasting and price optimization capabilities; and our push to modernize legacy features and functionality - Work on full stack technology, from prototyping to production ready models - Work with product, engineers, and data scientists to translate ideas into new products, services, features, and testing/evaluation processes - Expose and evangelize our science team capabilities by building microservices, applications, and coding agent plugins Preferred Qualifications - Experience with agent frameworks (e.g. ADK, LangChain) or modeling frameworks (e.g. Tensorflow, PyTorch, JAX) - Proficiency with data analysis / data science (SQL, pandas, sklearn, etc) - Ability to translate research work into practical proofs of concept Benefits - Competitive total rewards package including a base salary determined based on the role, experience, skill set, and location - Discretionary incentive compensation may be awarded in recognition of individual achievements and contributions for those in eligible roles - A range of benefits and programs to meet employee needs, based on eligibility