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Senior Machine Learning Engineer (AI Infrastructure)

Robinhood

Skills

Required

  • ML
  • software engineering
  • ML infrastructure / data engineering / model operations
  • Python
  • C++
  • TensorFlow / PyTorch
  • model serving
  • embedding
  • vector databases
  • distributed retrieval engines
  • Qdrant / ChromaDB / Elasticsearch

Preferred

  • modern ML infrastructure tooling
  • Ray
  • Kubeflow
  • SageMaker
  • TensorFlow Serving
  • Triton
  • observability
  • streamline workflows

Join our team as a Senior Machine Learning Engineer (AI Infrastructure) where you will lead the architecture and delivery of scalable systems for deploying, monitoring, and managing ML models in production. You will own the technical direction for key platform areas, drive cross-functional partnerships, evolve and scale our feature store, define and implement observability standards, manage cloud compute resources, and contribute to technical strategy and roadmap discussions. The ideal candidate will have strong proficiency in Python, C++, or similar languages, hands-on experience with ML frameworks, and 6+ years of software engineering experience with a focus on ML infrastructure, data engineering, or model operations.

Lead the architecture and end-to-end delivery of scalable systems for deploying, monitoring, and managing ML models in production.

Responsibilities

  • 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.

Requirements

  • 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
  • Experience influencing technical direction across teams and mentoring engineers at varying levels

Preferred

  • Solid knowledge of modern ML infrastructure tooling (e.g., Ray, Kubeflow, SageMaker, TensorFlow Serving, Triton)

Pay

USD 209,000 - 245,000

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