Robinhood
Senior Machine Learning Engineer, Applied ML
Robinhood, Menlo Park, California, United States, 94029
About the team + roleThe mission of the Applied Machine Learning team is to provide scalable data and model driven decision making solutions to the various business functions at Robinhood. We aim to create a personalized experience for our users, by helping them discover & engage with the right products & features within Robinhood that they might find most valuable. To accelerate progress, we are also building an accessible model development platform to democratize machine learning practices throughout the company. As we embark on this exciting journey, we are looking for a senior Machine Learning Engineer to join us to make this vision a reality.
What you'll doAs a Machine Learning Engineer on 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 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.
What you bring
5+ years of applied ML experience productionizing ML models with 2+ years focused on recommendations, ranking or personalization projects.
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.
Proven experience in ML with a
focus on ranking, recommendation systems, multi-objective optimization, and reinforcement learning .
Proficiency in Python, SQL, XGboost, PyTorch/TensorFlow.
Experience with Spark, Kafka, and Kubernetes is also desirable.
Ideally you have experience in the Finance sector.
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
#J-18808-Ljbffr
What you'll doAs a Machine Learning Engineer on 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 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.
What you bring
5+ years of applied ML experience productionizing ML models with 2+ years focused on recommendations, ranking or personalization projects.
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.
Proven experience in ML with a
focus on ranking, recommendation systems, multi-objective optimization, and reinforcement learning .
Proficiency in Python, SQL, XGboost, PyTorch/TensorFlow.
Experience with Spark, Kafka, and Kubernetes is also desirable.
Ideally you have experience in the Finance sector.
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
#J-18808-Ljbffr