Lead design and deployment of deep learning recommender systems.
CAI Stack provides cutting-edge pluggable building blocks for the entire AI stack. These components enable enterprises to build and scale machine learning (ML) and deep learning (DL) solutions, addressing diverse use cases. Our platform is utilized by some of the world's largest organizations to deploy vertical-specific products built on our proprietary AI infrastructure.
We are seeking a seasoned and hands-on Senior Data Scientist to lead the design, development, and deployment of our next-generation deep learning recommender systems. The ideal candidate will be a technical expert with a proven track record of building and optimizing large-scale recommender systems in a production environment. You will play a pivotal role in driving our business metrics by delivering highly personalized and relevant recommendations to our users.
Lead the end-to-end development of deep learning-based recommender systems, from ideation and research to production deployment and monitoring.
Design and implement multi-stage recommender system architectures, including efficient retrieval (candidate generation) and sophisticated ranking models.
Develop and optimize deep learning models for recommendations using architectures such as Two-Tower, Wide & Deep, and Transformer-based models.
Manage and create large-scale embedding layers for categorical features related to both users and products to effectively represent high-dimensional data.
Utilize and evaluate various specialized libraries and frameworks like TorchRec, TensorFlow Recommenders (TFRS), Microsoft Rec, Alibaba RecSys and NVIDIA Merlin to accelerate development and deployment.
Work with large, sparse datasets and tackle unique challenges in recommender systems, such as the cold-start problem and the long-tail problem.
Collaborate with data engineers, machine learning engineers, and product managers to ensure the entire recommendation pipeline is scalable, robust, and aligned with business goals.
Design and execute A/B tests to rigorously measure the business impact of new models and features.
Implement MLOps best practices for continuous integration, deployment, and monitoring of recommender systems in a production setting.
Mentor junior data scientists and contribute to a culture of technical excellence and innovation.
Hands-On Experience is a Must: Proven experience building, deploying, and maintaining deep learning recommender systems at scale.
Deep Learning Expertise: Extensive experience with deep learning frameworks such as PyTorch or TensorFlow.
Recommender System Architectures: Deep understanding and practical experience with architectures like:
Two-Tower Models: For efficient retrieval and candidate generation by learning separate embeddings for users and items.
Wide & Deep Models: For balancing the memorization of specific feature interactions with the generalization of a deep neural network.
Session-based and Sequential Models: For capturing the temporal dependencies and order of user behavior within a session or sequence of interactions.
Collaborative Filtering Architectures: For leveraging deep learning to model user-item interactions, such as in Neural Collaborative Filtering (NCF).
Graph Neural Networks (GNNs): For learning powerful item and user embeddings by representing interactions as a graph and propagating information.
Embedding Layers: For handling sparse, high-dimensional categorical features and creating dense vector representations at scale.
Specialized Libraries: Hands-on experience with at least two of the following libraries or frameworks:
TorchRec
TensorFlow Recommenders (TFRS)
NVIDIA Merlin
Microsoft Recommenders
NVIDIA Recommenders
Alibaba Recommenders
Scalability & MLOps: Demonstrated ability to build and deploy systems that handle terabytes of data with low latency. Experience with distributed training and real-time serving is highly valued.
Problem-Solving: A strong track record of solving common recommender system challenges such as the cold-start and long-tail problems.
Analytical Skills: Strong knowledge of experimental design and A/B testing methodologies.
Programming Proficiency: Expert-level proficiency in Python and its data science ecosystem (Pandas, NumPy).
Advanced degree (M.S. or Ph.D.) in a quantitative field such as Computer Science, Statistics, or a related discipline.
3 years of experience building recommendation systems at scale.
Experience with big data technologies (e.g., Spark, HDFS, Databricks) and vector databases.
Familiarity with other deep learning architectures for sequence modeling (e.g., Transformers).
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