Senior Machine Learning Engineer, Robinhood
AI Infrastructure
$209-245k
+ Bonus and equity
3+ days a week in office
Commission-free stock trading & investing app
Be an early applicant
Commission-free stock trading & investing app
1001+ employees
Be an early applicant
$209-245k
+ Bonus and equity
3+ days a week in office
1001+ employees
Company mission
To democratize finance for all.
Role
Who you are
- 6+ years of software engineering experience, with meaningful depth in ML infrastructure, data engineering, or model operations
- Demonstrated ability to own and deliver complex platform systems end-to-end, from architecture to production
- Deep expertise in model serving, distributed systems, and production ML workflows at scale
- Strong proficiency in Python, C++, or similar languages, and hands-on experience with ML frameworks such as TensorFlow or PyTorch
- Solid knowledge of modern ML infrastructure tooling (e.g., Ray, Kubeflow, SageMaker, TensorFlow Serving, Triton)
- 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
- Experience influencing technical direction across teams and mentoring engineers at varying levels
- Bachelor’s degree in Computer Science, Software Engineering, or a related technical field; advanced degree a plus
What the job involves
- 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
Our take
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.
Kirsty
Company Specialist at Welcome to the Jungle
Insights
Company
Funding (last 2 of 13 rounds)
Feb 2021
$2.4bn
CONVERTIBLE
Jan 2021
$1bn
LATE VC
Total funding: $5.6bn
Company HQ
Downtown Menlo Park, Menlo Park, CA
Leadership
Vlad Tenev
(Co-Founder, CEO & Chairman)Studied Mathematics at Stanford University, where they met Baiju. Dropped out of a PhD to start Robinhood. Alongside Robinhood, Vlad is also the co-founder and Executive Chairman of Harmonic.
Baiju B.
(Co-Founder)Studied Mathematics and Physics at Stanford University, where they met their Vlad and co-founded Robinhood in 2012. Baiju is also the Founder and CEO of Aetherflux.
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