onsite
Staff Machine Learning Engineer - The Estee Lauder Companies
ML Engineer
Lead end‑to‑end ML initiatives, building scalable models on AWS, deploying with Docker/Kubernetes, and driving data‑driven product improvements across global beauty brands.
About the role
Key Responsibilities
- Design, develop, and production‑grade machine learning models that power personalized product recommendations and supply‑chain optimizations.
- Own the full ML lifecycle: data ingestion, feature engineering, model training, validation, and deployment on AWS SageMaker and Kubernetes clusters.
- Collaborate with data scientists, software engineers, and product managers to translate business problems into scalable ML solutions.
- Implement MLOps best practices: CI/CD pipelines, automated testing, monitoring, and model versioning.
- Mentor junior engineers, conduct code reviews, and promote a culture of continuous learning and innovation.
Requirements
- 10+ years of experience in machine learning engineering, with a strong background in deep learning frameworks such as TensorFlow or PyTorch.
- Proficient in Python, SQL, and cloud services (AWS, SageMaker, S3, EC2).
- Hands‑on experience with containerization (Docker) and orchestration (Kubernetes) for model deployment.
- Deep understanding of MLOps principles, including CI/CD, monitoring, and model governance.
- Excellent communication skills and ability to work cross‑functionally in a fast‑paced, global environment.
Skills
machine learningpythontensorflowawsdockerkubernetes