Sr ML Engineer

Role Description

Identify new opportunities to improve business processes and improve consumer experiences, and prototype solutions to demonstrate value with a crawl, walk, run mindset.

• Work with data scientists and analysts to create and deploy new product features on the ecommerce website, in-store portals, and mobile app.
• Implement end-to-end solutions across the full breadth of ML model development lifecycle.
• Work hand in hand with scientists from the point of data exploration for model development to the point of building features.
• Opportunity to work on both batch and real-time models; role also involves operational support.
• Establish scalable, efficient, automated processes for data analyses, model development, validation, and implementation.
• Write efficient and scalable software 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.
• Contribute to and re-use community best practices.

Example Projects

• Customer Segmentation
• Automated text summarization and clustering
• Next-Best offer prediction
• Design Micro assortments for Next-Gen stores
• Anomaly detection and Root Cause Analysis
• Unified consumer profile with probabilistic record linkage
• Visual search for similar and complementary products

Qualifications

• University or advanced degree in engineering, computer science, mathematics, or a related field.
• 7+ years' experience developing and deploying machine learning systems into production, and independent contributor.
• Comfortable with Python ecosystem, vscode, jupyter notebooks.
• Experience working with big data tools: Hadoop, Spark, Kafka, etc.
• Experience with at least one cloud provider solution (AWS, GCP) and understanding of serverless code development (GCP preferred).
• Experience with object-oriented/object function scripting languages: Python, Java, C++, Scala, etc. (Python preferred).
• CI/CD expert; can work on GitHub actions, harness, Jenkins.
• Can work with Google Big Query, or similar warehouse.
• Work on Kubeflow pipelines independently and propose standards.
• Knowledge of Feature Engineering, Feature Store, and audit capabilities.
• 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.