Machine Learning Engineer

At Roche you can show up as yourself, embraced for the unique qualities you bring. Our culture encourages personal expression, open dialogue, and genuine connections,  where you are valued, accepted and respected for who you are, allowing you to thrive both personally and professionally. This is how we aim to prevent, stop and cure diseases and ensure everyone has access to healthcare today and for generations to come. Join Roche, where every voice matters.

At Roche, we are committed to delivering greater benefits to our patients by applying digital, data, machine learning, and AI capabilities to real-world operational challenges. The ML/MLOps Engineer is a hands-on technical contributor within the MLE/DE Cluster, focused on building, deploying, monitoring, and improving machine learning solutions for Pharma Technical Operations.

This role contributes to the end-to-end machine learning lifecycle, including data preparation, feature engineering, model training support, experiment tracking, model packaging, deployment pipelines, model serving, monitoring, and continuous improvement. The role works closely with data scientists, AI engineers, software engineers, data engineers, process experts, IT, quality, and business stakeholders to help deliver reliable, scalable, and compliant ML-enabled solutions in manufacturing, quality, supply chain, and technical operations environments.

The role also contributes to modern ML engineering capabilities, including data science-driven evaluation frameworks, reusable ML evaluation harnesses, model lifecycle management, reproducible experimentation, production monitoring, and MLOps practices that help move solutions from prototype to reliable production use.

Role description

The role is suited for a hands-on engineer who enjoys building robust, repeatable, and production-ready machine learning systems. The ML/MLOps Engineer Level 5 contributes to the development and operation of ML pipelines, model deployment workflows, evaluation frameworks, and monitoring capabilities under the guidance of senior engineers.

This role is expected to apply good software engineering and MLOps practices, support production readiness of ML solutions, and continuously build expertise in machine learning, cloud platforms, automation, monitoring, and regulated delivery environments.

ML Solution Delivery & MLOps

• ML pipeline development: Build and maintain components of machine learning pipelines, including data ingestion, preprocessing, feature generation, model training support, validation, packaging, and deployment.
• Model deployment: Support the deployment of ML models into cloud, hybrid, or application environments using reliable, repeatable, and automated deployment practices.
• Model serving: Contribute to model serving components, APIs, batch scoring workflows, and integration patterns that make ML outputs available to business applications and operational users.
• Experiment tracking: Use experiment tracking tools and structured documentation to support reproducibility, comparison of model versions, and evidence-based model improvement.
• Evaluation support: Contribute to data science-driven evaluation frameworks, including benchmark datasets, validation checks, model performance metrics, regression testing, and comparison of model outputs.
• ML evaluation harnesses: Build and maintain components of reusable evaluation harnesses that allow repeatable testing of models, data pipelines, features, scoring logic, and model performance across versions.
• Model monitoring: Support monitoring of deployed ML models, including performance, data quality, drift, latency, reliability, usage, and operational failure signals.
• Production support: Help investigate issues in ML pipelines and deployed models, support incident resolution, and contribute to continuous improvement of production ML systems.

Technical Expertise

• Software engineering: Apply solid Python development practices, Git-based collaboration, automated testing, code reviews, documentation, and maintainable code design.
• MLOps practices: Apply foundational MLOps concepts such as model versioning, model registry usage, experiment tracking, CI/CD, automated testing, deployment workflows, and monitoring.
• Data engineering foundations: Work with SQL, structured and semi-structured data, operational data sources, time-series data, and data quality checks needed for ML solutions.
• Machine learning foundations: Understand common ML workflows, including supervised learning, validation, feature engineering, model evaluation, model packaging, and model inference.
• Cloud and DevOps: Work with cloud and hybrid environments, preferably AWS, as well as Docker, CI/CD pipelines, infrastructure automation concepts, and runtime monitoring.
• Production reliability: Build ML components with reliability, traceability, reproducibility, observability, and maintainability in mind.
• Responsible and compliant delivery: Follow Roche standards, quality expectations, security requirements, data privacy expectations, and responsible AI practices, especially where ML solutions may support regulated environments.

Collaboration & Learning

• Cross-functional collaboration: Work with data scientists, AI engineers, software engineers, data engineers, IT, quality, process experts, and business stakeholders to understand use cases and support technical delivery.
• Bridge between data science and production: Help translate data science prototypes into robust, maintainable, and deployable ML solutions.
• Knowledge sharing: Actively learn new MLOps, ML engineering, cloud, and software engineering methods and share relevant insights with the MLE/DE Cluster.
• Growth mindset: Be curious, open to feedback, and willing to develop deeper expertise in machine learning, manufacturing, quality, and regulated production environments.
• AI and ML advocacy: Support effective and responsible adoption of machine learning and AI within Pharma Technical Operations.

Qualifications & Competencies

• Bachelor's degree in computer science, data science, engineering, mathematics, statistics, or a related field; or equivalent practical experience.
• Relevant experience building software, data, analytics, machine learning, or MLOps solutions.
• Strong hands-on Python skills and understanding of software engineering practices.
• Experience with Git, APIs, testing, documentation, and collaborative development.
• Exposure to machine learning workflows, including data preparation, feature engineering, model training, evaluation, deployment, and monitoring.
• Exposure to MLOps tools and practices such as MLflow, model registries, experiment tracking, CI/CD, Docker, automated testing, and model monitoring.
• Experience working with SQL and data pipelines.
• Exposure to cloud platforms, preferably AWS, and containerized deployment approaches.
• Ability to work in cross-functional teams and communicate technical topics clearly.
• Interest in building production-grade ML systems rather than one-off prototypes.
• Experience in industrial, manufacturing, pharmaceutical, biotechnology, quality, supply chain, or regulated environments is an advantage.

A healthier future drives us to innovate. Together, more than 100’000 employees across the globe are dedicated to advance science, ensuring everyone has access to healthcare today and for generations to come. Our efforts result in more than 26 million people treated with our medicines and over 30 billion tests conducted using our Diagnostics products. We empower each other to explore new possibilities, foster creativity, and keep our ambitions high, so we can deliver life-changing healthcare solutions that make a global impact.

Let’s build a healthier future, together.

Roche is an Equal Opportunity Employer.