Machine Learning Engineer

Elanco Tiergesundheit AG
Hook
3 weeks ago
Create job alert

As a global leader in animal health, we are dedicated to innovation and delivering products and services to prevent and treat disease in farm animals and pets. **At Elanco, we are driven by our vision of Food and Companionship Enriching Life and our purpose – all to Go Beyond for Animals, Customers, Society and Our People.***Your role:**As a Machine Learning (ML) Engineer at Elanco, you will be a key member of our engineering team, specialising in the end-to-end lifecycle of custom and third-party (including open source) machine learning models. 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.Your Responsibilities: Cross-Functional Collaboration: Work closely with data scientists, product managers, and software engineers to define requirements, integrate models into applications, and deliver impactful features. What You Need to Succeed (minimum qualifications): Advanced proficiency in Python and deep experience with core ML/data science libraries (e.g., PyTorch, TensorFlow, scikit-learn, pandas, NumPy).ML Model Deployment: Proven, hands-on experience deploying machine learning models into a production environment. Experience with MLOps tools and frameworks and containerisation 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). Proven experience with relevant DevSecOps concepts and tooling, including Continuous Integration/Continuous Delivery (CI/CD), Git SCM, Containerisation (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. Location: Hook, UK - Hybrid Work EnvironmentIf you think you might be a good fit for a role but don't necessarily meet every requirement, we encourage you to apply. You may be the right candidate for this role or other roles!*Elanco Animal Health Incorporated (NYSE: ELAN) is a global leader in animal health dedicated to innovating and delivering products and services to prevent and treat disease in farm animals and pets, creating value for farmers, pet owners, veterinarians, stakeholders, and society as a whole. With nearly 70 years of animal health heritage, we are committed to helping our customers improve the health of animals in their care, while also making a meaningful impact on our local and global communities. At Elanco, we are driven by our vision of Food and Companionship Enriching life and our Elanco Healthy Purpose CSR framework – all to advance the health of animals, people and the planet. Learn more at .
#J-18808-Ljbffr

Related Jobs

View all jobs

Machine Learning Engineer / MLOps Engineer

Machine Learning Engineering Lead

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.