Senior Machine Learning Infrastructure Engineer, Embedding Platform

What you'll do

• Design, train, and improve large-scale machine learning platforms for recommendation and personalization systems.
• Own major ML system components end-to-end from initial problem framing through production rollout.
• Build and optimize end-to-end pipelines for data preparation, feature generation, training, evaluation, and deployment.
• Improve distributed training efficiency and online inference performance for high-throughput systems.
• Apply modern modeling techniques, including sequence modeling and foundation models, to specific use cases.
• Develop reliable serving and monitoring patterns for low-latency production ML environments.
• Drive rigorous offline and online evaluations, including experimentation and model diagnostics.

What we're looking for

• 5+ years of experience in machine learning engineering focused on large-scale ML infrastructure and recommendation or personalization systems.
• Expertise in modern deep learning architectures, including sequence models and foundational models.
• Experience building or scaling ML platforms for large datasets and high-traffic production environments.
• Solid understanding of distributed training and inference concepts like data, model, or pipeline parallelism.
• Proficiency in Python and experience with modern ML frameworks such as PyTorch or TensorFlow.
• Strong software engineering fundamentals including system design, debugging, testing, and performance optimization.
• Experience with A/B testing, model evaluation frameworks, and real-time feedback loops in large-scale production systems.
• Excellent communication skills to present complex ML concepts to both technical and non-technical stakeholders.