Join a leading fintech company thatâs democratizing finance for all.
Robinhood was founded on a simple idea: that our financial markets should be accessible to all. With customers at the heart of our decisions, Robinhood is lowering barriers and providing greater access to financial information. Together, we are building products and services that help create a financial system everyone can participate in.
As we continue to build...
Weâre seeking curious, growth minded thinkers to help shape our vision, structures and systems; playing a key-role as we launch into our ambitious future. If youâre invigorated by our mission, values, and drive to change the world â weâd love to have you apply.
About the team + role
The Growth team is responsible for driving user & revenue growth for Robinhood. As we expand our suite of product offerings, we want to make sure we are taking a personalized approach to driving growth & engagement, by helping each user discover & engage with the right products & features within Robinhood that they might find most valuable. As we embark on this path, we are looking for a senior MLE to come in and lead our personalization efforts, and conceive, build & execute on a roadmap for how to effectively personalize our app experiences to drive user growth & engagement.
What You'll Do:
As a Machine Learning Engineer in our team, your primary focus will be on the implementation and evaluation of machine learning algorithms through rigorous experimentation and testing methodologies. Your responsibilities will include:
- Model Development and Implementation: Develop and fine-tune machine learning models, with a focus on ranking and scoring. Ensure these models are scalable and efficient.
- Development of Reinforcement Learning Models: Design and implement reinforcement learning algorithms to optimize decision-making processes in dynamic environments.
- 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.
- Multi-Armed Bandit Implementation: Apply multi-armed bandit strategies for real-time decision-making in our algorithmic processes. Balance the trade-off between exploration of new strategies and exploitation of known successful approaches.
- Bayesian Optimization Techniques: Utilize Bayesian optimization for hyperparameter tuning and model optimization. Focus on achieving higher efficiency in model selection and parameter optimization.
- 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 product managers to integrate machine learning models into the product and ensure they meet business requirements.
- Documentation and Reporting: Maintain comprehensive documentation of models, experiments, and findings. Prepare reports and presentations to communicate results to different stakeholders.
What you bring
- 3+ years of applied ML experience productionizing ML models.
- A fervent interest in exploring and applying AI and ML technologies.
- Strive to solve sophisticated engineering problems that drive business objectives.
- Solid technical foundation enabling active contribution to the design and execution of projects and ideas.
- Familiarity with architectural frameworks of large, distributed, and high-scale ML applications.
- Practical and demonstrable experience with ML algorithms within the space of multi-armed bandits, search relevance, ranking, advertisement targeting, or reinforcement learning.
- Proficiency in Python, k8s, PyTorch, or TensorFlow2.
- Ideally you have experience in the Finance sector.
- Experience with SQL, Spark, Kafka, and Flink is also desirable.
What we offer
- Market competitive and pay equity-focused compensation structure
- 100% paid health insurance for employees with 90% coverage for dependents
- Annual lifestyle wallet for personal wellness, learning and development, and more!
- Lifetime maximum benefit for family forming and fertility benefits
- Dedicated mental health support for employees and eligible dependents
- Generous time away including company holidays, paid time off, sick time, parental leave, and more!
- Lively office environment with catered meals, fully stocked kitchens, and geo-specific commuter benefits
The expected salary range for this role is based on the location where the work will be performed and is aligned to one of 3 compensation zones. This role is also eligible to participate in a Robinhood bonus plan and Robinhoodâs equity plan.
US Zone 1: $217000 - $255000
US Zone 2: $190000 - $224000
US Zone 3: $169000 - $199000
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. You can view comp zones for our US office locations in the table below. For other locations not listed, compensation can be discussed with your recruiter during the interview process.
Office locations (by comp zone)
US Zone 1: Menlo Park, CA; New York, NY; Seattle, WA; Washington, D.C.
US Zone 2: Denver, CO; Westlake (Dallas), TX; Chicago, IL
US Zone 3: Lake Mary, FL
Click here to learn more about Robinhoodâs Benefits.
Robinhood promotes diversity and provides equal opportunity for all applicants and employees. We are dedicated to building a company that represents a variety of backgrounds, perspectives, and skills. We believe that the more inclusive we are, the better our work (and work environment) will be for everyone. Additionally, Robinhood provides reasonable accommodations for candidates on request and respects applicants' privacy rights. To review Robinhood's Privacy Policy please visit Robinhood - US Applicant Privacy Policy. If you are an applicant located in the UK or EEA, please visit the Robinhood - UK/EEA Applicant Privacy Policy.