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Salary: USD 209,000 - 245,000 / annual
Robinhood is seeking a Senior Machine Learning Engineer to join the AI Infrastructure team, responsible for building and scaling the foundational ML platform that powers AI development and deployment across the company.
You will own the architecture and end-to-end delivery of scalable systems for deploying, monitoring, and managing ML models in production. Key responsibilities include:
- Lead architecture and delivery of production ML systems including model serving, feature store, and ML observability infrastructure
- Own technical direction for core platform areas from design through long-term reliability and scaling
- Drive cross-functional partnerships with ML practitioners, data engineers, and applied AI teams to reduce friction and accelerate experimentation
- Evolve and scale the 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
- Manage and optimize cloud compute resources (CPU/GPU) on AWS to support cost-effective, high-throughput training and inference
- Contribute to technical strategy and roadmap discussions; mentor engineers through design reviews and hands-on guidance
The role is based in Menlo Park, CA or Bellevue, WA with in-person attendance expected at least 3 days per week.
REQUIREMENTS:
- 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; hands-on experience with ML frameworks (TensorFlow, PyTorch)
- Solid knowledge of modern ML infrastructure tooling (Ray, Kubeflow, SageMaker, TensorFlow Serving, Triton)
- Hands-on experience with large-scale search systems, including embedding models, vector databases, and distributed retrieval engines (Qdrant, ChromaDB, Elasticsearch with dense vector search)
- Experience influencing technical direction across teams and mentoring engineers at varying levels
- Bachelor's degree in Computer Science, Software Engineering, or related technical field (advanced degree a plus)
