Rokt is an ecommerce technology company with the mission of making every transaction more relevant. The Rokt Ecommerce Network leverages proprietary machine learning recommendation systems, powering billions of transactions for hundreds of millions of customers, and is trusted to do this by companies like Live Nation, Fanatics, Macy's, AMC Theatres, PayPal, Uber, Hulu, Staples, Albertsons and HelloFresh.
Key Responsibilities
Collaborate closely with product managers and other engineers to understand business priorities, frame machine learning problems, and architect machine learning solutions for smart bidding, lookalike modelling, forecasting, and related ranking and prediction tasks.
Build and productionise machine learning models, including model-specific data pipelines, feature engineering within the team's feature store, and integration with the team's orchestration and serving infrastructure.
Evaluate model performance through offline metrics, and monitor deployed models for drift, leading retraining or rollback decisions as needed.
Contribute to and maintain the high quality of the code base with tests that provide a high level of functional coverage as well as non-functional aspects such as load testing, unit testing, and integration testing.
Keep track of emerging tech and trends, research the state-of-the-art deep learning models, prototype new modelling ideas, and conduct offline and online experiments
Required Qualifications
PhD or Master's in Computer Science, Statistics, Mathematics, or related field with specialization in ML, AI, or Information Retrieval (or equivalent experience)
Extensive knowledge in and experience with some of the following areas: Bayesian methods, recommender systems, multi-task modelling, meta-learning, click-through rate modelling or conversion rate modelling
3+ years of industry experience building production-grade machine learning systems, spanning model training, tuning, deployment, serving, and monitoring
Experience with Kubeflow (or similar), TensorFlow, and a feature store in a production environment is a massive plus
Bonus points if you are familiar with any of the following architectures or have experience with the models mentioned: DCNV2, MMOE, Deep & Wide, ESMM, xDeepFM, and GDCN
Benefits
Willingness to work 4 day in-office, 1 day remote weekly schedule.
