R
Robinhood
Senior Machine Learning Engineer (AI Infrastructure)
Skills
Data EngineeringPythonPyTorchTensorFlowMachine LearningAIAWSElasticsearch
1 month ago
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Important information
Contract type:
Permanent
Salary:
$209,000 – $245,000 / year
Location:
Bellevue, WA, USA
Work mode:
Onsite
Published on:
26 September 2026
What they need
- 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- Strong proficiency in Python, C++, or similar languages, and hands-on experience with ML frameworks such as TensorFlow or PyTorch
- Demonstrated ability to own and deliver complex platform systems end-to-end, from architecture to production
- 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
- 6+ years of software engineering experience, with meaningful depth in ML infrastructure, data engineering, or model operations
- Deep expertise in model serving, distributed systems, and production ML workflows at scale
- Bachelor’s degree in Computer Science, Software Engineering, or a related technical field; advanced degree a plus
- Solid knowledge of modern ML infrastructure tooling (e.g., Ray, Kubeflow, SageMaker, TensorFlow Serving, Triton)
- Experience influencing technical direction across teams and mentoring engineers at varying levels
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