Software Engineer 3

What You’ll Do

• Design and build components of a multi-tenant inference platform integrated directly with MongoDB Atlas, supporting semantic search and hybrid retrieval
• Collaborate with AI engineers and researchers to productionize inference for embedding models and rerankers — enabling both batch and real-time use cases. Contribute to platform capabilities such as latency-aware routing, model versioning, health monitoring, and observability
• Improve performance, autoscaling, GPU utilization, and resource efficiency in a cloud-native environment
• Work across product, infrastructure, and ML teams to ensure the inference platform meets the scale, reliability, and latency demands of Atlas users
• Gain hands-on experience with tools like vLLM and container orchestration with Kubernetes

Who You Are

• 2+ years of experience building backend or infrastructure systems at scale
• Strong software engineering skills in languages such as Go, Rust, Python, or C++, with an emphasis on performance and reliability
• Experienced in cloud-native architectures, distributed systems, and multi-tenant service design
• Familiar with concepts in ML model serving and inference runtimes, even if not directly deploying models
• Knowledge of vector search systems (e.g., Faiss, HNSW, ScaNN) is a plus
• Comfortable working across functional teams, including ML researchers, backend engineers, and platform teams
• Motivated to work on systems integrated into MongoDB Atlas and used by thousands of developers

Nice to Have

• Experience integrating infrastructure with production ML workloads
• Understanding of hybrid retrieval, prompt-driven systems, or retrieval-augmented generation (RAG)
• Contributions to open-source infrastructure for ML serving or search