5 applied ml engineer jobs at 3 companies in Apache Junction, AZ
🚀PromotedHiringCafe
ML Engineer - Inference & Model Deployment
Cupertino, CA, US
$250k-$310k/yrOn-SiteFull Time
HiringCafe: Building a 100× better job search engine to take on Indeed and LinkedIn.
Turn powerful AI and ML models into fast, reliable production systems. Own inference latency, throughput, model-serving architecture, multi-GPU systems, and production deployment for millions of users.
HiringCafe: Building a 100× better job search engine to take on Indeed and LinkedIn.
Build the ML and AI search behind HiringCafe — ranking, recommenders, retrieval, and LLM agents that surface jobs people would never find on their own.
Senior Machine Learning Engineer, Applied AI & Data
Scottsdale, Arizona, United States
HybridFull Time
GitKraken: Developer experience platform for software collaboration and productivity.
Senior-level, hands-on experience in applied machine learning/AI with a track record of shipping ML-powered product features; able to scope ambiguous problems, prototype quickly, integrate models into production, define metrics, and communicate with cross-functional partners.
American ExpressNYSE: AXP: Global financial services and credit card payment network.
8+ YOE8+ years software engineering experience with production LLM or applied ML systems; strong backend, APIs, data pipelines, cloud (AWS/GCP), Kubernetes, and agentic AI tooling; product orientation and ownership.
American ExpressNYSE: AXP: Global financial services and credit card payment network.
5+ YOE5+ years software engineering experience with production LLM or applied ML systems; strong backend, APIs, data pipelines, and cloud infrastructure skills; experience shipping AI/agentic systems and owning production reliability.
American ExpressNYSE: AXP: Global financial services and credit card payment network.
4+ YOE4+ years software engineering experience; hands-on work with LLMs/AI features or applied ML; strong backend skills in Python, Go, or TypeScript; familiarity with APIs, cloud (AWS/GCP), Kubernetes, and modern development practices.