Senior AI/ML Engineer

JOB Title - Senior AI/ML Engineer
Location - Seattle Wa


Job Overview

We are seeking a Senior AI/ML Engineer to lead the design and development of the advanced AI systems that make our Customer Data Platform (CDP) the authoritative source of truth for customer data - covering the entire US adult population.

This role owns the intelligence layer of CDP: production-grade identity resolution at massive scale, and LLM-powered interfaces that make trusted customer data accessible to every stakeholder in the organization. You will architect systems that resolve billions of customer records into accurate, unified profiles - and build the natural language interfaces that let business users query and understand that data without writing SQL.

You will drive architecture decisions, define best practices, and lead the development of systems where accuracy, trust, and timeliness are non-negotiable.

Job Responsibilities - Identity Resolution

Design and lead end-to-end identity resolution architecture, combining probabilistic models, ML, and embedding-based techniques to build the authoritative customer identity graph

Build and optimize large-scale entity matching systems across billions of records and multiple data domains - ensuring every US adult is accurately represented in CDP

Architect advanced candidate generation and blocking strategies (LSH, phonetic encoding, semantic similarity) that balance precision with computational feasibility at population scale

Design high-precision matching pipelines using ensemble approaches (rules + ML + LLM-based validation) to maximize accuracy of golden customer profiles

Develop scalable clustering and graph-based approaches for unified customer identity resolution with clear confidence scoring and auditability

Lead implementation of embedding pipelines and similarity search systems using transformer models for semantic-level identity matching

Job Responsibilities - AI/LLM

Architect and build LLM-powered systems for entity resolution, including zero-shot and few-shot classification workflows that handle edge cases traditional models miss

Design and implement RAG-based architectures for enriching and contextualizing customer data from unstructured sources

Lead development of NLQ-to-SQL platforms, enabling business users to query CDP - the authoritative source of truth - using natural language

Translate ambiguous business questions into structured queries with schema awareness, semantic layers, and guardrails that protect data integrity

Define best practices for prompt engineering, evaluation, and LLM observability - ensuring AI outputs meet the trust standards CDP demands

Design and optimize vector search architectures (Pinecone, Qdrant, pgvector) for large-scale retrieval across customer data

Evaluate and integrate emerging frameworks such as LangChain, LangGraph, and agentic workflows where they strengthen CDP capabilities

Education and Work Experience

Bachelor's or Master's degree in Computer Science, Data Science, or related field

6+ years of experience in ML/AI engineering

Proven experience building production-grade entity resolution or identity graph systems at scale

Experience designing LLM-based applications in enterprise environments with high accuracy and trust requirements

Technical Skills

Advanced programming: Python

Deep expertise in ML algorithms for similarity, classification, and clustering - particularly in identity resolution contexts

Strong experience with transformer models, embeddings, and semantic search at population scale

Hands-on experience with LLM APIs and orchestration frameworks

Strong SQL and experience with distributed data processing (Spark, Dask)

Experience with vector databases and ANN search systems (FAISS, Pinecone, etc.)

Expertise in ML lifecycle management (MLflow or equivalent)

Understanding of data governance, privacy, and security requirements for customer identity data

Knowledge, Skills, and Abilities

Strong system design and architectural thinking for AI/ML systems at population scale

Ability to balance precision, recall, and scalability in identity resolution systems - understanding that accuracy directly impacts CDP's authority as the source of truth

Strong understanding of data semantics and customer domain modeling across diverse data sources

Leadership in driving AI engineering best practices, standards, and quality benchmarks

Ability to collaborate across data engineering, product, security, and business teams to deliver trusted customer intelligence