Senior Software Engineer, Inference Platform
MongoDB · Palo Alto, CA · PTO Atlas Search
About this role
MongoDB is hiring a senior-level Software Engineer based in Palo Alto, CA (hybrid). The posting calls out experience with Python, Rust, AWS, GCP and roughly 5+ years of relevant work. Compensation is listed at $126,000–$248,000 per year.
- Role
- Software Engineer
- Function
- software engineering
- Level
- senior
- Track
- Individual contributor
- Employment
- Full-time
- Location
- Palo Alto, CA
- Work mode
- Hybrid
- Experience
- 5+ years
- Department
- PTO Atlas Search
More roles at MongoDB
Job description
from MongoDB careersAbout the Role
We’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.
We're looking to speak with candidates in Palo Alto for our hybrid working model.
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