Senior Machine Learning Engineer (AI Infrastructure)

Robinhood; Menlo Park (CA); On-site; USD 140,000 - 210,000; Full time

Job summary

Robinhood in Menlo Park is seeking a senior ML Platform Engineer to lead the architecture and end-to-end delivery of scalable systems for deploying, monitoring, and managing ML models in production.

You will own the technical direction for key platform areas — including model serving, the feature store, and ML observability infrastructure — from design through long-term reliability, while partnering with ML practitioners and data engineers to streamline workflows and accelerate experimentation.

Qualifications

• 6+ years of software engineering experience with strong ML infrastructure
• Solid experience with model serving and production ML workflows
• Experience with large-scale search systems and vector databases

Responsibilities

• Lead architecture and delivery of scalable systems for deploying and monitoring ML models
• Own technical direction for model serving, feature store, and observability infra
• Collaborate with ML practitioners and data engineers to streamline workflows and accelerate experimentation
• Scale feature store for fast real-time and batch feature retrieval
• Define observability standards for model performance and data pipelines
• Optimize cloud compute (CPU/GPU) on AWS for cost-effective training and inference

Skills

Python

C++

ML frameworks

Production ML

Education

Bachelor's degree

Tools

Qdrant

ChromaDB

Elasticsearch

TensorFlow Serving

Job 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

Strong proficiency in Python, C++, or similar languages, and hands-on experience with ML frameworks such as TensorFlow or PyTorchDemonstrated ability to own and deliver complex platform systems end-to-end, from architecture to productionHands-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 capabilities6+ years of software engineering experience, with meaningful depth in ML infrastructure, data engineering, or model operationsDeep expertise in model serving, distributed systems, and production ML workflows at scaleBachelor's degree in Computer Science, Software Engineering, or a related technical field; advanced degree a plusSolid 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