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Machine Learning Engineer Intern, Applied ML (Summer 2026)

Robinhood

Machine Learning Engineer Intern, Applied ML (Summer 2026)

Robinhood

Bellevue, WA; Menlo Park, CA; New York, NY

·

On-site

·

Full-time

·

1mo ago

Compensation

$99,840 - $99,840

Benefits & Perks

Learning and development stipend

Flexible PTO policy

Parental leave program

Remote work flexibility

Health, dental, and vision coverage

Top Tier compensation with equity

Required Skills

Python

Apache Spark

SQL

Join us in building the future of finance.

Our mission is to democratize finance for all. An estimated $124 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.

The mission of the Applied Machine Learning team is to develop scalable, data- and model-driven solutions that enhance decision-making across Robinhood. We focus on personalizing user experiences, helping customers discover and engage with the most valuable products and features. To empower ML adoption company-wide, we’re also building accessible model development tools that democratize machine learning at Robinhood.

We’re looking for a passionate and curious Machine Learning Intern to join us in advancing this mission and learning alongside a world-class team of ML engineers.

This role is based in our Menlo Park, CA office, with in-person attendance expected at least 3 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

  • Build and Prototype ML Models: Work on early-stage ranking, recommendation, and personalization models using techniques like collaborative filtering, content-based filtering, or learning-to-rank approaches.

  • Experimentation & Evaluation: Assist in setting up and analyzing A/B tests and offline experiments to evaluate the effectiveness of ML algorithms.

  • Explore and Analyze Data: Dive into rich datasets to extract signals and support data-driven feature engineering and model tuning.

  • Collaborate Cross-Functionally: Work with engineers, data scientists, and product managers to understand business needs and help integrate ML into real product use cases.

  • Contribute to Internal Tools: Help improve model development tools, document findings, and support reusable libraries that enable broader adoption of ML practices.

What You Bring

  • Pursuing a degree in Computer Science, Data Science, Statistics, Engineering, or a related technical field, with an expected graduation date in Winter 2026 or Spring 2027.

  • Solid foundation in machine learning principles, algorithms, and data structures.

  • Familiarity with Python and basic ML frameworks (e.g., scikit-learn, Py Torch, or Tensor Flow).

  • Interest in recommendation systems, personalization, or reinforcement learning.

  • Enthusiasm for working with large datasets and running experiments to learn what works.

  • A collaborative mindset and strong communication skills.

What we offer

  • Market competitive compensation structure

  • Quarterly lifestyle wallet for personal wellness, learning and development, and more!

  • Time away including company holidays, paid time off, and sick time!

  • Lively office environment with catered meals, fully stocked kitchens, and geo-specific commuter 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 hourly range for this role is based on the location where the work will be performed and is aligned to one of 3 compensation zones. For other locations not listed, compensation can be discussed with your recruiter during the interview process.

Zone 1 (Menlo Park, CA; New York, NY; Bellevue, WA; Washington, DC)$48—$48 USDZone 2 (Denver, CO; Westlake, TX; Chicago, IL)$42—$42 USDZone 3 (Lake Mary, FL; Clearwater, FL; Gainesville, FL)$37—$37 USD

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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About Robinhood

Robinhood

Robinhood

Public

Democratizing finance for all.

1,001-5,000

Employees

Menlo Park

Headquarters

$32B

Valuation

Reviews

3.2

2 reviews

Work Life Balance

3.0

Compensation

3.0

Culture

2.0

Career

2.5

Management

2.0

15%

Recommend to a Friend

Pros

Senior leadership involved in interview process

Cons

Restricted trading access for users

Removed buy button for GameStop stock

Lack of transparency in hiring process

Salary Ranges

6 data points

Junior/L3

L3/Junior

L4/Mid

Mid/L4

Senior/L5

Junior/L3 · Software Engineer

1 reports

$77,000

total / year

Base

-

Stock

-

Bonus

-

$77,000

$77,000

Interview Experience

5 interviews

Difficulty

3.6

/ 5

Duration

21-35 weeks

Offer Rate

20%

Experience

Positive 20%

Neutral 60%

Negative 20%

Interview Process

1

Application Review

2

Recruiter Screen

3

Technical Phone Screen

4

Onsite/Virtual Interviews

5

Team Matching

6

Offer

Common Questions

Coding/Algorithm

System Design

Behavioral/STAR

Technical Knowledge