Machine Learning Engineer Jobs

About The Position

Requirements

• Bachelor’s degree or foreign equivalent in Computer Science or related field and three years of experience in job offered or related occupation.
• 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
• Spark, Kafka, or Kubernetes.
• Background checks required

Responsibilities

• Implement and evaluate machine learning algorithms through rigorous experimentation and testing methodologies.
• Evaluate cutting technologies, including transformer-based model architecture and large foundational models, to identify solutions for Robinhood specific problems.
• 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.
• Design reinforcement learning algorithms and apply multi-armed bandit strategies to optimize decision-making in dynamic environments.
• Design and conduct A/B tests to assess the performance of different machine learning models, including setting up the test environment, monitoring performance, and analyzing results.
• Analyze experimental data to extract actionable insights using statistical techniques to validate findings.
• 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.
• 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.

Benefits

• Challenging, high-impact work to grow your career
• Performance driven compensation with multipliers for outsized impact, bonus programs, equity ownership, and 401(k) matching
• 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.