Machine Learning Engineer
Robinhood · Bellevue, WA
## Join us in building the future of finance.
Our mission is to democratize finance for all. An estimated
Our mission is to democratize finance for all. An estimated
24 trillion of assets will be inherited by younger generations in the next two decades. The largest transfer of wealth in human history. If you’re ready to be at the epicenter of this historic cultural and financial shift, keep reading.
## About the team + role
We are building an elite team, applying frontier technologies to the world’s biggest financial problems. We’re looking for bold thinkers. Sharp problem-solvers. Builders who are wired to make an impact. Robinhood isn’t a place for complacency, it’s where ambitious people do the best work of their careers. We’re a high-performing, fast-moving team with ethics at the center of everything we do. Expectations are high, and so are the rewards. We're looking for an exceptional Machine Learning Engineer to help shape the future of our core platforms, products, and customer experiences. FinTech is one of the most complex and rapidly evolving spaces in technology, and the challenges we're tackling require deep innovation, critical thinking, and scale that don't always have strong precedents.
You'll take on a highly influential role shaping vision and execution across key strategic initiatives. You'll partner with cross-functional leaders, contribute to high-impact decisions, guide complex projects from concept to completion, and mentor others on the team. This is a role for someone who leverages modern tools and cutting-edge methodologies as a core part of how they solve problems, and raises the bar for everyone around them.
This role is based in our Bellevue, WA, with in-person attendance expected at least three days per week.
At Robinhood, we believe in the power of in-person work to accelerate progress, spark innovation, and strengthen community. Our office experience is intentional, energizing, and designed to fully support high-performing teams.
## What you'll do
As a Machine Learning Engineer on the AI Research and Development team, the primary focus will be on the implementation and evaluation of machine learning algorithms through rigorous experimentation and testing methodologies.
The responsibilities will include:
• AI and ML Research : Evaluate cutting technologies, including but not limited to, transformer-based model architecture and large foundational models to identify solutions for Robinhood specific problems.
• Model Development and Implementation : Develop and implement scalable machine learning models focusing on advanced ranking and recommendation systems, including expertise in Collaborative Filtering, Content Based Filtering, and Hybrid models, alongside proficiency in Learning to Rank (LTR) techniques for effective prioritization. Additionally, design reinforcement learning algorithms and apply multi-armed bandit strategies to optimize decision-making in dynamic environments, balancing exploration and exploitation.
• A/B Testing and Experimentation : Design and conduct A/B tests to assess the performance of different machine learning models. This includes setting up the test environment, monitoring performance, and analyzing results.
• Data Analysis and Insight Generation : Analyze experimental data to extract actionable insights. Use statistical techniques to validate the findings and ensure their relevance and accuracy.
• Cross-Functional Collaboration : Work closely with other engineering teams, data scientists, and the marketing team to integrate machine learning models into the product and ensure they meet business requirements. Present results to different stakeholders.
• Tooling and Documentation : Build reusable libraries for common machine learning practices. Offer support and guidance to the usage of these tools. Maintain comprehensive documentation of libraries, models, experiments, and findings.
• Telecommuting permitted.
## What you bring
• Bachelor’s degree or foreign equivalent in Computer Science or related field and three years (3) of experience in job offered or related occupation. Alternatively, a Masters in Computer Science or related field and one year (1) of experience in job offered or related occupation
• Education and/or experience must include:
• Productionisation of ML models with focus on recommendations, ranking, or personalization;
• Model development with classical ML techniques for tabular data;
• Model development with modern ML techniques for sequential data;
• Hands-on experience with architectural frameworks of large, distributed, and high-scale ML applications;
• Produce robust business outcomes through comprehensive AB test and rigorous statistical analysis;
• Proficiency in Python, SQL, XGBoost, Pytorch or Tensorflow to carry out production ready projects; and
• Spark, Kafka, or Kubernetes.
• Background checks required.
## What we offer
• Challenging, high-impact work to grow your career
• Performance driven compensation with multipliers for outsized impact, bonus programs, equity ownership, and 401(k) matching
• Best in class benefits to fuel your work, including 100% paid health insurance for employees with 90% coverage for dependents
• Lifestyle wallet - a highly flexible benefits spending account for wellness, learning, and more
• Employer-paid life & disability insurance, fertility benefits, and mental health benefits
• Time off to recharge including company holidays, paid time off, sick time, parental leave, and more!
• Exceptional office experience with catered meals, events, and comfortable workspaces.
In addition to the base pay range listed below, this role is also eligible for bonus opportunities + equity + benefits.
Base pay for the successful applicant will depend on a variety of job-related factors, which may include education, training, experience, location, business needs, or market demands. The expected base pay range for this role is based on the location where the work will be performed and is aligned to the corresponding compensation zone.
Base Pay Range:
## About the team + role
We are building an elite team, applying frontier technologies to the world’s biggest financial problems. We’re looking for bold thinkers. Sharp problem-solvers. Builders who are wired to make an impact. Robinhood isn’t a place for complacency, it’s where ambitious people do the best work of their careers. We’re a high-performing, fast-moving team with ethics at the center of everything we do. Expectations are high, and so are the rewards. We're looking for an exceptional Machine Learning Engineer to help shape the future of our core platforms, products, and customer experiences. FinTech is one of the most complex and rapidly evolving spaces in technology, and the challenges we're tackling require deep innovation, critical thinking, and scale that don't always have strong precedents.
You'll take on a highly influential role shaping vision and execution across key strategic initiatives. You'll partner with cross-functional leaders, contribute to high-impact decisions, guide complex projects from concept to completion, and mentor others on the team. This is a role for someone who leverages modern tools and cutting-edge methodologies as a core part of how they solve problems, and raises the bar for everyone around them.
This role is based in our Bellevue, WA, with in-person attendance expected at least three days per week.
At Robinhood, we believe in the power of in-person work to accelerate progress, spark innovation, and strengthen community. Our office experience is intentional, energizing, and designed to fully support high-performing teams.
## What you'll do
As a Machine Learning Engineer on the AI Research and Development team, the primary focus will be on the implementation and evaluation of machine learning algorithms through rigorous experimentation and testing methodologies.
The responsibilities will include:
• AI and ML Research : Evaluate cutting technologies, including but not limited to, transformer-based model architecture and large foundational models to identify solutions for Robinhood specific problems.
• Model Development and Implementation : Develop and implement scalable machine learning models focusing on advanced ranking and recommendation systems, including expertise in Collaborative Filtering, Content Based Filtering, and Hybrid models, alongside proficiency in Learning to Rank (LTR) techniques for effective prioritization. Additionally, design reinforcement learning algorithms and apply multi-armed bandit strategies to optimize decision-making in dynamic environments, balancing exploration and exploitation.
• A/B Testing and Experimentation : Design and conduct A/B tests to assess the performance of different machine learning models. This includes setting up the test environment, monitoring performance, and analyzing results.
• Data Analysis and Insight Generation : Analyze experimental data to extract actionable insights. Use statistical techniques to validate the findings and ensure their relevance and accuracy.
• Cross-Functional Collaboration : Work closely with other engineering teams, data scientists, and the marketing team to integrate machine learning models into the product and ensure they meet business requirements. Present results to different stakeholders.
• Tooling and Documentation : Build reusable libraries for common machine learning practices. Offer support and guidance to the usage of these tools. Maintain comprehensive documentation of libraries, models, experiments, and findings.
• Telecommuting permitted.
## What you bring
• Bachelor’s degree or foreign equivalent in Computer Science or related field and three years (3) of experience in job offered or related occupation. Alternatively, a Masters in Computer Science or related field and one year (1) of experience in job offered or related occupation
• Education and/or experience must include:
• Productionisation of ML models with focus on recommendations, ranking, or personalization;
• Model development with classical ML techniques for tabular data;
• Model development with modern ML techniques for sequential data;
• Hands-on experience with architectural frameworks of large, distributed, and high-scale ML applications;
• Produce robust business outcomes through comprehensive AB test and rigorous statistical analysis;
• Proficiency in Python, SQL, XGBoost, Pytorch or Tensorflow to carry out production ready projects; and
• Spark, Kafka, or Kubernetes.
• Background checks required.
## What we offer
• Challenging, high-impact work to grow your career
• Performance driven compensation with multipliers for outsized impact, bonus programs, equity ownership, and 401(k) matching
• Best in class benefits to fuel your work, including 100% paid health insurance for employees with 90% coverage for dependents
• Lifestyle wallet - a highly flexible benefits spending account for wellness, learning, and more
• Employer-paid life & disability insurance, fertility benefits, and mental health benefits
• Time off to recharge including company holidays, paid time off, sick time, parental leave, and more!
• Exceptional office experience with catered meals, events, and comfortable workspaces.
In addition to the base pay range listed below, this role is also eligible for bonus opportunities + equity + benefits.
Base pay for the successful applicant will depend on a variety of job-related factors, which may include education, training, experience, location, business needs, or market demands. The expected base pay range for this role is based on the location where the work will be performed and is aligned to the corresponding compensation zone.
Base Pay Range:
61,138 -
00,000 per year
To Apply: Apply by clicking APPLY NOW. Indicate job code 10035097 in your application.
Click here to learn more about our Total Rewards, which vary by region and entity.
If our mission energizes you and you’re ready to build the future of finance, we look forward to seeing your application.
Robinhood provides equal opportunity for all applicants, offers reasonable accommodations upon request, and complies with applicable equal employment and privacy laws. Inclusion is built into how we hire and work—welcoming different backgrounds, perspectives, and experiences so everyone can do their best. Please review the Privacy Policy for your country of application.
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Public study materials
- Python (Python)
- Python Fundamentals · basic
- Control Flow · basic
- Strings · basic
- Collections / Data Structures · basic
- Functions · basic
- Modules and Packages · basic
- File Handling · basic
- Exception Handling · basic
- Object-Oriented Programming (OOP · medium
- Advanced Python Concepts · advance
- Functional Programming · advance
- Multithreading & Multiprocessing · advance
- Async Programming · advance
- System Architecture (System Architecture)
- Fundamentals of System Architecture · basic
- Distributed System Basics · basic
- System Reliability Concepts · basic
- Scaling Concepts · basic
- Networking Basics · basic
- Web Communication · basic
- API Communication · basic
- Proxy & Delivery Systems · basic
- Web Architecture Basics · basic
- Rendering Architectures · basic
- Frontend Advanced Concepts · basic
- Message Queue Basics · medium
- What is Load Balancer · medium
- Load Balancing Algorithms · medium
- API Design Basics · medium
- API Protection · medium
- Authentication Basics · medium
- Security Tokens · medium
- Security Threats · medium
- Encryption & Security · medium
- SQL Database Basics · medium
- SQL Scaling Concepts · medium
- NoSQL Databases · medium
- Database Optimization · medium
- Replication Strategies · medium
- Caching Basics · medium
- Cache Storage Systems · medium
- Cache Strategies · medium
- Event-Driven Systems · advance
- Queue Reliability · advance
- Microservices Basics · advance
- Microservice Communication · advance
- Distributed Transactions · advance
- DevOps Basics · advance
- Automation Tools · advance
- Deployment Strategies · advance
- Monitoring Basics · advance
- Monitoring Tools · advance
- Distributed System Concepts · advance
- Distributed Algorithms · advance
- Express.js — Web APIs & middleware (expressjs)
- Application setup · basic
- Routing deep dive · basic
- 1. MVC / Layered Architecture · medium
- Validation · medium
- File uploads · medium
- Sessions & auth (stateful) · medium
- Passport & strategies · medium
- Templating & SSR · medium
- WebSockets & SSE · medium
- Security middleware · advance
- Reverse proxies & trust · advance
- Performance · advance
- API design & versioning · advance
- Testing with Supertest · advance
- GraphQL & tRPC (overview) · advance
- Deployment checklist · advance
- Middlewares · basic
- Request & Response · basic
- System Designing (System Designing)
- Day-1 : What is system Designing ? · basic
- Day-2 : Vertical vs. Horizontal Scaling · basic
- Day-3:How to do vertical scaling ? · basic
- Day4:How to do horizaontal scaling ? · basic
- Day:5TCP vs UDP · basic
- Day6:IP & DNS · basic
- Day7:Client-Server Model · basic
- Day8:HTTP & HTTPS · basic
- Databases (SQL vs NoSQL) · medium
- Caching · medium
- Day9:Latency & Throughput · basic
- Load Balancing · medium
- Indexes & Query Optimization · medium
- CDN · medium
- Proxies · medium
- Message Queues · medium
- Horizontal vs Vertical Scaling · medium
- Database Replication · advance
- Database Sharding · advance
- Consistent Hashing · advance
- CAP Theorem · advance
- Rate Limiting · advance
- Service Discovery · advance
- Event-Driven Architecture · advance
- API Gateway · advance
- Distributed Consensus · expert
- Microservices · expert
- Observability · expert
- Idempotency · expert
- PACELC Theorem · expert
- Two-Phase Commit · expert
- Back-of-Envelope Estimation · expert
- Designing for Failure · expert
- SQL (SQL)
- SQL Fundamentals · basic
- Database Operations · basic
- Table Operations · basic
- CRUD Operations · basic
- Filtering & Operators · basic
- SQL Functions · basic
- GROUPING Data · basic
- Joins · medium
- Constraints · basic
- Subqueries · medium
- Set Operators · medium
- Views · medium
- Indexes · medium
- Normalization · advance
- Transactions · advance
- Stored Procedures & Functions · advance
- Triggers · advance
- Advanced SQL · advance
- Query Optimization · advance
- Database Design · advance
- SQL Security · advance
- Backup & Recovery · advance
- Questions · interview questions
- Django (Django)
- What is Django · basic
- Installing Django · basic
- Features of Django · basic
- MVT Architecture · basic
- Django vs Flask · basic
- Creating Project & Creating App · basic
- Django Project Structure · basic
- URL Routing · basic
- Views · basic
- Templates · basic
- Static & Media Files · medium
- Models · medium
- ORM (Object Relational Mapping) · medium
- Model Relationships · medium
- Migrations · medium
- Django Admin · medium
- Forms · medium
- Authentication · medium
- Authorization · medium
- Middleware · medium
- Signals · medium
- Class Based Views Deep Dive · advance
- Generic Views · advance
- File Handling · advance
- Django REST Framework (DRF) · advance
- Advanced ORM · advance
- Caching · advance
- Asynchronous Django · advance
- Background Tasks · advance
- Interview Questions · interview-questions
- JavaScript (JavaScript)
- JS Introduction · basic
- Variables & Data Types · basic
- Operators · basic
- Control Flow · basic
- Functions · basic
- Scope & Execution · basic
- Closures · basic
- Objects · basic
- Arrays · basic
- Strings · basic
- DOM Manipulation · basic
- Browser APIs · medium
- Asynchronous JavaScript · medium
- Fetch & APIs · medium
- ES6+ Features · medium
- OOP in JavaScript · medium
- Prototype & Inheritance · advance
- Advanced Functions · advance
- Memory Management · advance
- Error Handling · advance
- Modules · advance
- Advanced Async Concepts · advance
- Functional Programming · advance
- JavaScript Internals · advance
- Performance Optimization · advance
- Angular (Angular)
- Angular Fundamentals · basic
- Project Structure · basic
- Components & Templates · basic
- Data Binding · basic
- Directives · basic
- Pipes · basic
- Component Communication · basic
- Lifecycle Hooks · basic
- Routing Basics · basic
- Routing · basic
- API Calls · basic
- Forms · medium
- Routing · medium
- Services & Dependency Injection · medium
- RxJS & Observables · medium
- Authentication & Security · medium
- Component Interaction · medium
- State Management Basics · medium
- Error Handiling · medium
- Perfomance Basic · medium
- Real World Features · medium
- Advanced Angular Architecture · advance
- Change Detection · advance
- Advanced RxJS · advance
- State Management · advance
- Dynamic Rendering · advance
- Perfomance Optimization · advance
- Modern Angular · advance
- STAR (Situation, Task, Action, and Result) (Situation Based Questions)
- Node.js — Server-side JavaScript (nodejs)
- Getting started · basic
- JavaScript on the server · basic
- CommonJS modules · basic
- ES modules (ESM) · basic
- npm & package management · basic
- Asynchronous JavaScript in Node · basic
- The event loop · basic
- Essential core utilities · basic
- process & configuration · basic
- File system basics · basic
- HTTP & HTTPS servers · medium
- Streams · medium
- Events & EventEmitter · medium
- Advanced filesystem · medium
- crypto · medium
- Compression & encoding · medium
- Child processes · medium
- net, dgram & DNS · medium
- readline, timers & scheduling · medium
- Testing & diagnostics (intro) · medium
- Worker threads · advance
- cluster & multi-process scaling · advance
- Performance & tuning · advance
- Debugging & observability · advance
- Security hardening · advance
- Native addons & N-API · advance
- Architecture patterns · advance
- Graceful shutdown · advance
- 100 Questions · interview questions
- MongoDB — Documents & data modeling (MongoDB)
- Introduction · basic
- Shell, Compass & tools · basic
- Databases & collections · basic
- CRUD operations · basic
- Indexes deep dive · medium
- Explain plans & performance · medium
- Aggregation framework · medium
- Schema design patterns · medium
- Mongoose basics · medium
- Mongoose advanced · medium
- Drivers & connection · medium
- Operators for updates & arrays · medium
- Replication & read preferences · advance
- Write concern & read concern · advance
- Multi-document transactions · advance
- Change streams · advance
- Sharding (overview) · advance
- Atlas Search & full-text · advance
- GridFS & large files · advance
- Backup, restore & ops · advance
- AWS Crash Course (AWS)
- What is Cloud ? · basic
- What is AWS ? · basic
- If not cloud ? · basic
- Cloud Computing · basic
- AWS Pricing · basic
- AWS Shared Responsibility Model · basic
- AWS Management Console · basic
- AWS SDKs · basic
- AWS IAM · medium
- Users, Groups, Roles · medium
- Policies · medium
- AWS Organizations · medium
- AWS Cognito · medium
- AWS Directory Service · medium
- AWS KMS (Key Management Service) · medium
- AWS Secrets Manager · medium
- AWS Shield · medium
- AWS WAF · medium
- AWS Inspector · medium
- AWS GuardDuty · medium
- EC2 · advance
- Launching EC2 Instances · advance
- EBS Volumes · advance
- Security Groups · advance
- Key Pairs · advance
- Elastic IP · advance
- User Data Scripts · advance
- Auto Scaling · advance
- Load Balancers · advance
- ALB · advance
- NLB · advance
- Serverless Compute ,AWS Lambda, Lambda Layers · advance
- Event-Driven Architecture · advance
- ECS · advance
- EKS · advance
To Apply: Apply by clicking APPLY NOW. Indicate job code 10035097 in your application.
Click here to learn more about our Total Rewards, which vary by region and entity.
If our mission energizes you and you’re ready to build the future of finance, we look forward to seeing your application.
Robinhood provides equal opportunity for all applicants, offers reasonable accommodations upon request, and complies with applicable equal employment and privacy laws. Inclusion is built into how we hire and work—welcoming different backgrounds, perspectives, and experiences so everyone can do their best. Please review the Privacy Policy for your country of application.