Senior Machine Learning Engineer (AI Infrastructure) Robinhood
Plateforme de création de vidéos AI
CDI
Menlo Park, CA
Télétravail occasionnel
209K à 245K $ par an
> 5 ans
Les compétences que l’on cherche
C++
TensorFlow
Pytorch
Elasticsearch
Sagemaker
+3
Votre nouvelle entreprise
4491 collaborateurs
Finanzas
L'avis de Welcome
For years, traditional brokers ruled the share trading realm, charging hefty fees that locked out everyday investors. Robinhood emerged as a disruptor, offering commission-free trading and opening the door to a new generation. Since then, expectations have continued to evolve, with investors looking not just to lower costs, but for simpler tools, clearer information, and guidance that makes the market easier to understand. Robinhood was built to meet those changing expectations, designed from the start as a mobile-first platform that makes investor more accessible and less intimidating for people at every stage of their financial journey. Through its app, users can trade stocks and other financial instruments while benefiting from a clean interface and built-in educational support, helping them move from curiosity to confidence without unnecessary complexity. Recently, Robinhood has strenghtned its impact too, particularly in the UK. With the launch of AI-powered Digests within its Robinhood Cortex assistant that helps users understand what's driving stock movements in plain English, while upcoming options trading and expanded educational resources add flexibility and depth. Together, these steps position Robinhood as a platform evolving alongside its users, blending technology and education to stay ahead in an ever-changing investment landscape.
Vos missions au quotidien
- 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
Ce que l’on cherche
- 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