Senior Machine Engineer, Consumer Edge

• The ideal candidate is a hands-on problem-solver who thrives on end-to-end ownership—from collaborating on project scope and defining data needs to developing novel modeling strategies and deploying robust, scalable systems onto our cloud infrastructure (GCP & AWS)
• 3+ years of hands-on experience building and deploying machine learning models in a production environment
• Proven, demonstrable experience in Natural Language Processing (NLP) with a specific focus on entity resolution, record linkage, or data matching projects
• Strong proficiency in Python and common ML/data science libraries (e.g., scikit-learn, pandas, spaCy, Hugging Face Transformers)
• Hands-on experience with ML deployment and data processing services on public cloud providers (GCP, AWS, or Azure)
• Solid software engineering fundamentals, including version control (Git), testing, and CI/CD practices
• Excellent written and verbal communication skills, with a proven ability to document design decisions and present complex information clearly

Desirable

• We are seeking an experienced Senior Machine Learning Engineer with a specialization in Natural Language Processing (NLP) to solve one of our most critical and complex data challenges: entity resolution. In this role, you will design, build, and deploy production-scale systems to intelligently link records from disparate, massive datasets
• Your work will be foundational, creating a single, unified view of core entities (such as products, organizations, locations, and customers) that powers our analytics, product features, and business strategy
• Design & Build: Lead the end-to-end development of machine learning pipelines for large-scale entity resolution, record linkage, and data matching
• NLP Modeling: Apply and customize advanced NLP techniques (e.g., entity extraction, semantic similarity, text vectorization, fuzzy matching) to compare and match entities from structured and unstructured text
• System Architecture: Engineer scalable and efficient data processing and model inference systems designed to handle terabyte scale datasets using cloud-native tools
• Deployment: Deploy, monitor, and maintain ML models and data pipelines in production on GCP (e.g., Vertex AI, BigQuery, Dataflow)
• Project Leadership: Collaborate closely with product managers, data engineers, and business stakeholders to scope new projects, define data requirements, and establish success metrics
• Communication & Documentation: Create clear, comprehensive design documents and effectively communicate complex technical concepts, trade-offs, and results to both technical and non-technical audiences