Senior Machine Learning Engineer (AI Infrastructure)

Robinhood; Menlo Park (CA); On-site; USD 200,000 - 320,000; Full time

Job summary

Robinhood 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 model serving, the feature store, and observability infrastructure, collaborating with ML practitioners, data engineers, and AI teams to accelerate experimentation.

We expect 6+ years in software engineering with ML infrastructure, deep expertise in distributed systems and

Qualifications

• 6+ years of software engineering experience with ML infrastructure, data engineering, or model operations.
• Strong proficiency in Python and C++ or similar languages.
• Hands-on experience with ML frameworks such as TensorFlow or PyTorch.
• Experience with large-scale search/vector DB systems (e.g., Qdrant, ChromaDB, Elasticsearch).
• Knowledge of ML infrastructure tooling (Ray, Kubeflow, SageMaker, TensorFlow Serving, Triton).
• Ability to influence technical direction and mentor engineers across teams.

Responsibilities

• Lead the architecture and end-to-end delivery of scalable ML production systems.
• Own the technical direction for model serving, feature store, and ML observability infrastructure.
• Partner with ML practitioners, data engineers, and applied AI teams to streamline workflows and accelerate experimentation.
• Evolve and scale the feature store for real-time and batch use cases.
• Define observability standards for model performance, data pipelines, and feature freshness.
• Manage cloud compute resources (AWS) for cost-effective training and inference at scale.
• Contribute to technical strategy and mentor engineers through reviews and hands-on guidance.

Skills

Python

C++

TensorFlow

PyTorch

ML Infra

Distributed systems

Vector databases

Qdrant

Elasticsearch

AWS

Model serving

Observability

Education

Bachelor’s degree in CS/SE or related field

Advanced degree a plus

Tools

Kubeflow

Ray

SageMaker

TensorFlow Serving

Triton

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 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