Applied Machine Learning Engineer

Best Buy•Richfield, MN

2d•Hybrid

About The Position

As the Applied Machine Learning Engineer, you’ll build and operate the ML systems behind Best Buy’s search, product discovery, gift recommendations, and homepage personalization — experiences used by millions of customers every day. This is a hands-on role spanning the full ML lifecycle: building training pipelines for data scientists, productionizing models as low-latency services, and owning monitoring and alerting in production. You’ll partner with ML Scientists and Data Engineers to turn research into reliable, scalable systems and keep them running smoothly. This role is hybrid, which means you will work some days at our corporate office in Richfield, Minnesota, and some days virtually from home or another non-Best Buy location. The specific work arrangements vary by role and team. The recruiter or hiring manager will provide more details during the hiring process.

Requirements

Bachelor’s degree in Computer Science, Engineering, Math, or related field (or equivalent experience)
3 years of experience in ML/AI, data engineering, or software development
3 years of experience building, deploying, and serving real-time ML/AI models with ultra-low latency and high throughput, as resilient, scalable, cloud native services
2 years of experience building data engineering pipelines, ML/AI driven products or other related functions (e.g. software engineering, data science)
2 years of Python and SQL skills
2 years of experience in software design, test-driven development, and implementation using a general-purpose programming language (e.g., Python, Scala, Rust, C++, etc.)
Solid understanding of supervised/unsupervised learning, deep learning, and Gen AI

Nice To Haves

Master’s degree in a quantitative field plus 2 years experience in ML/AI
5 years of professional experience
Experience with GCP: Vertex AI, Cloud Run, Cloud Functions, BigQuery, or GCS
Hands-on with ML model serving (FastAPI, Vertex AI endpoints, or similar) and Docker/Kubernetes
Familiarity with embeddings, vector search, or information retrieval systems
Experience with monitoring and observability for production ML systems
Exposure to PyTorch, Hugging Face Transformers, or LLMs/generative AI in production

Responsibilities

Build and maintain ML training pipelines and infrastructure (Vertex AI Pipelines, BigQuery, GCS) that enable data scientists to train and evaluate models
Productionize ML models as real-time, low-latency services using Cloud Run, Vertex AI, and containerized microservices
Set up and own monitoring, logging, and alerting for production ML services
Develop feature engineering, embedding, and data pipelines using GCP data services
Partner with Data Scientists and ML Researchers to take models from research to production and iterate based on real-world performance
Build and maintain the Assistant AI product-discovery runtime: FastAPI agents, retrieval and enrichment against Search service, and turning model output into customer-facing responses