Machine Learning (ML) Engineer

As a Machine Learning (ML) Engineer at Elanco, you will be a key member of our engineering team, specializing in the end-to-end lifecycle of custom and third-party (including open-source) machine learning models. You will translate complex business problems into scalable, production-ready AI solutions. This role is focused on the practical application of machine learning, requiring a strong blend of software engineering discipline and deep ML expertise to design, build, and deploy models that deliver real-world value.

Strategic Priorities

• Pipeline Acceleration: Optimize the search and approval of high-impact medicines with a focus on speed, cost and precision.
• Manufacturing Excellence: Improve the efficiency, quality and consistency of core manufacturing processes, specifically execution and equipment effectiveness.
• Sales Effectiveness: Simplify the process to find, trust and consume relevant customer insights that drive sales growth and improved engagement.
• Productivity: Expand operating margin through efficiency by systematically reducing our operating expenses across the company, improving profitability.

Your Role: Responsibilities

• Custom Model Development: Design, build, and train bespoke ML models tailored to specific business needs, from initial prototype to full implementation.
• Third-Party Model Utilization: Identify, tune and deploy third-party ML models, covering proprietary and open-source models.
• Production Deployment: Manage the deployment of ML models into our production environments, ensuring they are scalable, reliable, and performant.
• MLOps and Automation: Build and maintain robust MLOps pipelines for Continuous Integration / Continuous Delivery (CI/CD), model monitoring, and automated retraining.
• Data Pipeline Construction: Collaborate with data engineers/stewards to build and optimize data pipelines that feed ML models, ensuring data quality and efficient processing for both training and inference.
• Cross-Functional Collaboration: Work closely with data scientists, product managers, and software engineers to define requirements, integrate models into applications, and deliver impactful features.
• Code and System Quality: Write clean, maintainable, and well-tested production-grade code. Uphold high software engineering standards across all projects.
• Performance Tuning: Monitor and analyze model performance in production, identifying opportunities for optimization and iteration.

Qualifications

• Education: A Bachelor's or Master's degree in Computer Science, Software Engineering, Artificial Intelligence, or a related quantitative field.
• Required Experience: 3+ years experience in Machine Learning/Engineering or relevant work.
• Programming Excellence: Advanced proficiency in Python and deep experience with core ML/data science libraries (e.g., PyTorch, TensorFlow, scikit-learn, pandas, NumPy).
• Software Engineering Fundamentals: Strong foundation in software engineering principles, including data structures, algorithms, testing, and version control (Git).
• ML Model Deployment: Proven, hands-on experience deploying machine learning models into a production environment.
• MLOps Tooling: Experience with MLOps tools and frameworks and containerization technologies (Docker, Kubernetes).
• Cloud Platform Proficiency: Practical experience with Public Cloud, specifically Microsoft Azure and Google Cloud Platform (GCP) and their ML services (e.g., Azure ML, Vertex AI).

Preferred Qualifications

• DevSecOps: Proven experience with relevant DevSecOps concepts and tooling, including Continuous Integration / Continuous Delivery (CI/CD), Git SCM, Containerization (Docker, Kubernetes), Infrastructure-as-Code (HashiCorp Terraform).
• Machine Learning Theory: Solid understanding of the theoretical foundations of machine learning algorithms, including deep learning, NLP, and classical ML.
• Problem-Solving: A pragmatic and results-oriented approach to problem-solving, with the ability to translate ambiguous requirements into concrete technical solutions.
• Industry Experience: A broad understanding of life science, covering the business model, regulatory/compliance requirements, risks and rewards. An ability to identify and execute against opportunities within machine learning that directly support life science outcomes.
• Communication: Excellent communication skills, capable of articulating complex technical decisions and outcomes to both technical and non-technical stakeholders.