Senior Machine Learning Engineer (Recommender Systems)

Role overview

Design and operate recommendation systems that personalize experiences at enterprise scale. You’ll own the work across retrieval, ranking, real-time inference, experimentation, and production operations, balancing relevance with response speed and system capacity.

Responsibilities

- Build multi-stage pipelines for candidate retrieval, ranking, and re-ranking or filtering. - Develop and tune recommendation models, including collaborative filtering, matrix factorization, two-tower architectures, and deep learning approaches. - Implement vector-based retrieval with approximate nearest neighbor search and connect it to real-time scoring services. - Establish offline evaluation, A/B testing, and production monitoring for model quality and business outcomes. - Improve latency and throughput through techniques such as caching, quantization, and hardware acceleration. - Own deployment and operational workflows for recommendation models and their supporting features.

Requirements

- At least five years of hands-on machine learning and software engineering experience, including production recommender systems at scale. - Strong Python skills and practical experience with frameworks such as PyTorch or TensorFlow and recommendation tools or libraries. - Experience with multi-stage recommendations, ANN or vector search, feature stores, and model-serving frameworks. - Familiarity with cloud platforms, MLOps, SQL, and large-scale feature engineering with tools such as Apache Spark. - Advanced written and spoken English, with the ability to explain technical options to cross-functional partners.

Benefits and work setup

The role is fully remote. The package includes paid time off, equity participation, an annual retreat, a winter break, education support, optional in-person gatherings, and individualized career development.