Role overview
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.
What you'll work on
- 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 we're looking for
- 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.
Tags & focus areas
Used for matching and alerts on DevFound Machine Learning Ai Dev Tensorflow Pytorch Python Spark