Senior Machine Learning Infrastructure Engineer, Embedding Platform

Role overview This is a senior engineering role focused on the infrastructure that produces, stores, and serves machine-learning embeddings for downstream personalization and retrieval systems. The work blends hands-on systems engineering with collaboration alongside applied scientists and product teams. The position is suited for an experienced engineer who wants to deepen ownership of ML platform components while contributing to design decisions.

Responsibilities - Build and maintain components of an embedding platform, including training pipelines, feature storage, and low-latency serving layers. - Operate and improve vector retrieval infrastructure, addressing scale, latency, and reliability requirements. - Collaborate with applied scientists and ML engineers to productionize new embedding models and use cases. - Contribute to on-call coverage, monitoring, and incident response for embedding services. - Participate in design reviews, write clear technical documentation, and share best practices with partner teams.

Requirements - Several years of experience building production ML infrastructure, distributed systems, or data platforms. - Proficiency in at least one systems-level language such as Python, Scala, Java, or Go. - Familiarity with model training workflows, feature stores, and online inference patterns. - Working knowledge of vector search technologies and retrieval architectures. - Ability to work independently on complex engineering problems while coordinating across teams.