About the RoleWe're looking for a Senior Engineer to help build the next-generation inference platform that supports embedding models used for semantic search, retrieval, and AI-native experiences in MongoDB Atlas.You'll join the broader Search and AI Platform organization and collaborate with ML researchers and engineers from our Voyage.ai acquisition. Together, we're building infrastructure for real-time, low-latency, and high-scale inference — fully integrated with Atlas and designed for developer-first experiences.As a Senior Engineer, you'll focus on building core systems and services that power model inference at scale. You'll own key components of the infrastructure, work across teams to ensure tight integration with Atlas, and contribute to a platform designed for reliability, performance, and ease of use.What You'll DoDesign and build components of a multi-tenant inference platform integrated directly with MongoDB Atlas, supporting semantic search and hybrid retrievalCollaborate with AI engineers and researchers to productionize inference for embedding models and rerankers — enabling both batch and real-time use casesContribute to platform capabilities such as latency‑aware routing, model versioning, health monitoring, and observabilityImprove performance, autoscaling, GPU utilization, and resource efficiency in a cloud-native environmentWork across product, infrastructure, and ML teams to ensure the inference platform meets the scale, reliability, and latency demands of Atlas usersGain hands‑on experience with tools like vLLM and container orchestration with KubernetesWho You Are5+ years of experience building backend or infrastructure systems at scaleStrong software engineering skills in languages such as Go, Rust, Python, or C++ , with an emphasis on performance and reliabilityExperienced in cloud-native architectures, distributed systems, and multi‑tenant service designFamiliar with concepts in ML model serving and inference runtimes , even if not directly deploying modelsKnowledge of vector search systems (e.g., Faiss, HNSW, ScaNN) is a plusComfortable working across functional teams, including ML researchers, backend engineers, and platform teamsMotivated to work on systems integrated into MongoDB Atlas and used by thousands of developersNice to HaveExperience integrating infrastructure with production ML workloadsUnderstanding of hybrid retrieval, prompt‑driven systems, or retrieval‑augmented generation (RAG)Contributions to open‑source infrastructure for ML serving or search#J-18808-Ljbffr