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# Machine Learning Engineer, AI Infra Organization

- **Company:** Robinhood
- **Location:** Bellevue, WA, United States
- **Experience:** Expert
- **Salary:** $209,000.0 - $245,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, C++ (Programming Language), Cloud Computing, Information Engineering, Database Design, Distributed Systems, Elasticsearch, Python (Programming Language), Machine Learning, Tensorflow, Azure Machine Learning, Search Technologies, Software Engineering, Scripting, Graphics Processing Unit (GPU), Pytorch, Large Language Models, Kubernetes, Information Technology, Data Management, Machine Learning Operations, Multiplatform, Data Pipelines
- **Published:** September 23, 2026
- **Apply:** https://www.careerbuilder.com/job-details/senior-machine-learning-engineer-ai-infra-bellevue-wa--7e3bc469-e962-4746-a97b-10ebd920654c

## About the Role

* 6+ years of software engineering experience, with meaningful depth in ML infrastructure, data engineering, or model operations
 * Demonstrated ability to own and deliver complex platform systems end-to-end, from architecture to production
 * Deep expertise in model serving, distributed systems, and production ML workflows at scale
 * Strong proficiency in Python, C++, or similar languages, and hands-on experience with ML frameworks such as TensorFlow or PyTorch
 * Solid knowledge of modern ML infrastructure tooling (e.g., Ray, Kubeflow, SageMaker, TensorFlow Serving, Triton)
 * Hands-on experience with large-scale search systems, including embedding models, vector databases, and distributed retrieval engines using platforms such as Qdrant, ChromaDB, or Elasticsearch with dense vector search capabilities
 * Experience influencing technical direction across teams and mentoring engineers at varying levels
 * Bachelor's degree in Computer Science, Software Engineering, or a related technical field; advanced degree a plus, Amazon Web Services (AWS), Artificial Intelligence (AI), C++ Programming Language, CPU (Central Processing Unit), Cloud Computing, Computer Science, Cross-Functional, Data Management, Database Design, Distributed Computing, Elasticsearch, Engineering, Finance, GPU (Graphics Processing Unit), High Reliability, High Throughput, Large-Scale Systems, Machine Learning, Machine Tool, Mentoring, Multiplatform/Cross-Platform, Performance Modeling, Problem Solving Skills, Product Engineering, Python Programming/Scripting Language, Software Engineering, Systems Scalability, Team Building, Team Lead/Manager, Technical Strategy, Use Cases

## Description

* Lead the architecture and end-to-end delivery of scalable systems for deploying, monitoring, and managing ML models in production
 * Own the technical direction for key platform areas - including model serving, the feature store, and ML observability infrastructure - from design through long-term reliability
 * Drive cross-functional partnerships with ML practitioners, data engineers, and applied AI teams to streamline workflows, reduce friction, and accelerate experimentation
 * Evolve and scale our feature store to support efficient, low-latency feature retrieval across real-time and batch use cases
 * Define and implement robust observability standards for model performance, data pipelines, and feature freshness across the ML platform
 * Manage and optimize cloud compute resources (CPU/GPU) on AWS to support cost-effective, high-throughput training and inference at scale
 * Contribute to technical strategy and roadmap discussions, and help mentor engineers on the team through design reviews and hands-on guidance


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