Lead Machine Learning Engineer (Personalization & AI Models)

Job description

We are seeking a Lead Machine Learning Engineer (Personalization & AI Models) to pioneer the building and optimization of user segmentation, recommendation, and embedding models within our expansive personalization system for a Mobile App.

The focus areas will include multi-vector representations, real-time model inference, and the integration of personalization workflows with cutting-edge technologies including AWS Personalize SDK, PGVector, and RudderStack.

Responsibilities

- Develop and optimize embedding models using sentence-transformer models for user profiles and personalization

- Implement KNN-based recommendation systems for real-time content scoring and ranking

- Utilize AWS Personalize SDK to train and deploy machine learning models that dynamically adapt to user behavior

- Integrate embedding models with Databricks, RudderStack, and AWS services to ensure real-time profile updates

- Fine-tune ML models aimed at boosting revenue predictions, enhancing user engagement, and refining audience segmentation

- Optimize PGVector and Redis for efficient vector-based lookups and caching

- Collaborate with data engineers to devise ML pipelines for robust training, validation, and deployment processes

Requirements

- Strong experience in Machine Learning, Deep Learning, and AI-driven personalization

- Proficiency in Python, PyTorch, and TensorFlow

- Expertise in vector-based search and recommendation systems, including knowledge of KNN, PGVector, Redis

- Hands-on experience with AWS Personalize SDK and SageMaker or similar ML training pipelines

- Familiarity with Databricks, Delta Lake, and Apache Spark for large-scale model training and deployment

- Strong understanding of real-time personalization, A/B testing, and user segmentation models

- Capability to work with event-driven architectures and implement real-time feature engineering

- Fluent English communication skills at a B2+ level

Nice to have

- Knowledge of ML model monitoring, MLOps, and automation of ML pipelines through CI/CD

- Experience with Graph Neural Networks (GNNs) for analyzing user similarity and clustering

- Familiarity with deployment of real-time analytics tools and dashboarding, such as Looker, Tableau, or Snowflake