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# Machine Learning Engineer, AI Infra

- **Company:** Robinhood
- **Location:** Menlo Park, CA, 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, Distributed Systems, Elasticsearch, Python (Programming Language), Tensorflow, Azure Machine Learning, Search Technologies, Software Engineering, Pytorch, Large Language Models, Kubernetes, Information Technology, Machine Learning Operations, Data Pipelines
- **Published:** September 21, 2026
- **Apply:** https://startup.jobs/senior-machine-learning-engineer-ai-infra-robinhoodapp-10134823

## 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

## 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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