Job Location: Europe or Latin America
Calling all originals: At Levi Strauss & Co., you can be yourself — and be part of something bigger. We’re a company of people who like to forge our own path and leave the world better than we found it. Who believe that what makes us different makes us stronger. So add your voice. Make an impact. Find your fit — and your future.
Be a pioneer in the fashion industry by joining our global Data, Analytics & AI "startup with assets," where you will have the chance to build exciting solutions to help our Americas business and at the same time be part of a bigger, across-continents, data community.
As the Senior Machine Learning Engineer, you will work alongside the Data Science team to operationalize the Machine Learning and Generative AI (GenAI) models in Production on a broad set of domains that power a data-driven transformation of our standard business procedures across channels and organizations. You will develop and deploy novel approaches to optimize existing AI systems to maximize their value and increase consumer satisfaction at every brand touchpoint.
The Senior Machine Learning Engineer will report directly to the Direct to Customer (DTC) AI Engineering Manager.
While our global headquarters are in San Francisco, this role is open to remote candidates across Europe and Latin America (LATAM), with a preference for Spain, Italy, or Mexico.
About the Job
Implement end-to-end solutions for batch and real-time AI and GenAI algorithms along with requisite tooling around monitoring (e.g., New Relic), logging, automated testing, model retraining, model deployment (Cloud Run, Kubernetes), and metadata tracking.
Identify new opportunities to improve business processes and consumer experiences, and prototype solutions to demonstrate value with a crawl, walk, run mindset, specifically focusing on Agentic AI and Automated-to-Automated (A2A) workflows.
Work with data scientists and analysts to create and deploy new product features on the ecommerce website, in-store portals, and the Levi's mobile app.
Establish scalable, efficient, automated MLOps processes for data analyses, model development, validation, and implementation.
Write efficient and scalable software, leveraging advanced Python and cloud-native services, to ship products in an iterative, continual-release environment.
Contribute to and promote good software engineering practices across the team and build cloud native software for ML pipelines (e.g., Dataproc, Dataflow).
Contribute to and re-use community best practices.
Embody the values and passions that characterize Levi Strauss & Co., with empathy to engage with colleagues from multiple backgrounds.
Besides driving the transformation of Levi's into a data-driven enterprise in general, here are some specific projects you will work on and contribute to:
Next-Generation Personalization: Building and optimizing real-time product recommenders (e.g., Similar Items, Complementary Recommenders, OOS Recommender).
Visual Commerce & Outfitting: Features like Shop the Look and developing services for Outfitting Inspiration and Generation.
GenAI & Agentic AI: Prototyping and productionizing the TailoredAI Assistant—an Agentic AI layer leveraging multimodal intelligence (Image, Voice, and Text Aware) for personalized styling and shopping support.
Loyalty & Customer AI: Architecting and deploying models for Next Best Action, Email Targeting, and sophisticated Customer Segmentation.
About You
University or advanced degree in engineering, computer science, mathematics, or a related field
7+ years experience developing and deploying machine learning systems into production
Experience working with a variety of relational SQL and NoSQL databases
Experience working with big data tools: Hadoop, Spark, Kafka, Dataproc etc.
Experience with at least one cloud provider solution (AWS, GCP, Azure) and understanding of severless code development
Experience with object-oriented/object function scripting languages: Python, Java, C++, Scala, etc.
Previous experience developing predictive models in a production environment, MLOps and model integration into larger scale applications.
Experience with Machine and Deep Learning libraries such as Scikit-learn, XGBoost, MXNet, TensorFlow or PyTorch
Experience with AI/GenAI concepts, including Agentic AI architectures (e.g., LangGraph, Google adk), and automating workflows.
Knowledge of data pipeline and workflow management tools
Expertise in standard software engineering methodology, e.g. unit testing, test automation, continuous integration, code reviews, design documentation
Working experience with native ML orchestration systems such as Kubeflow, Step Functions, MLflow, Airflow, TFX...
Relevant working experience with Docker and Kubernetes is a big plus