Senior ML Platform Engineer, AI Infrastructure

Robinhood; Menlo Park (CA); Hybrid; USD 209,000 - 245,000; Full time

Benefits offered by this job

Health insurance

Equity ownership

401(k) matching

Bonus programs

Access to AI tools

Job summary

Robinhood seeks a Senior Software Engineer for its AI Infrastructure team in Menlo Park, CA (and Bellevue, WA). You will own architecture and end-to-end delivery of foundational ML platform components, including model serving, feature store, and training infrastructure, collaborating with ML practitioners to accelerate experiments.

This role requires 6+ years in software engineering with ML infra, strong Python/C++, and experience with distributed systems.

Qualifications

• 6+ years of software engineering with ML infrastructure focus.
• Own end-to-end platform systems from design to production.
• Deep expertise in model serving and production ML workflows at scale.
• Proficient in Python and C++, with ML frameworks like TensorFlow or PyTorch.

Responsibilities

• Lead architecture and delivery of scalable ML deployment, monitoring, and management systems.
• Own direction for model serving, feature store, and observability infrastructure.
• Collaborate with ML practitioners, data engineers, and AI teams to streamline workflows.
• Scale feature store for real-time and batch use cases with low latency.
• Define robust observability standards for model performance and data pipelines.
• Manage cloud compute resources (CPU/GPU) on AWS for cost-effective training and inference.
• Mentor engineers and contribute to technical strategy and roadmaps.

Skills

ML infrastructure

Distributed systems

Python

C++

TensorFlow/PyTorch

Model serving

Mentoring

Education

Bachelor’s degree in Computer Science / Software Engineering

Tools

Qdrant

ChromaDB

Elasticsearch

Ray

Kubeflow

SageMaker

TensorFlow Serving

Job description

Robinhood seeks a Senior Software Engineer for its AI Infrastructure team in Menlo Park, CA (and Bellevue, WA). You will own architecture and end-to-end delivery of foundational ML platform components, including model serving, feature store, and training infrastructure, collaborating with ML practitioners to accelerate experiments.

This role requires 6+ years in software engineering with ML infra, strong Python/C++, and experience with distributed systems.