Software Engineer jobs at Mongodb in United States, San Francisco

• Proficient in modern programming languages and techniques
• Eager to impact and envision the technological future of a complex system
• Experienced in developing distributed systems, cloud services and SaaS products
• Experienced in designing and maintaining search platforms or distributed databases is preferred but not required
• Experienced in debugging and profiling multithreaded JVM applications is preferred but not required

• Build and design our integrated search platform, written in Java
• Contribute to two leading open source platforms, MongoDB and Lucene
• Participate in product and design discussions
• Perform code reviews with peers and make recommendations on how to improve our code and software development processes
• Collaborate with other teams including cloud services, database, enterprise tools, drivers and support to coordinate changes or contribute to their projects

• In 3 months, you'll contribute to the improvement of our core "mongoT" repository
• In 6 months, you'll be working on features such as query features or platform improvements
• In 12 months, you'll be building POCs, designing new features, and collaborating with other teams from across the company on complex projects

As a Staff Engineer, you’ll be hands-on with design and implementation, while working with engineers across experience levels to build a robust, scalable system. The focus is on latency, availability, observability, and scalability in a multi-tenant, cloud-native environment.

• Partner with Search Platform and Voyage.ai AI engineers and researchers to productionize state-of-the-art embedding models and rerankers, supporting both batch and real-time inference
• Lead key projects around performance optimization, GPU utilization, autoscaling, and observability for the inference platform
• Design and build components of a multi-tenant inference service that integrates with Atlas Vector Search, driving capabilities for semantic search and hybrid retrieval
• Contribute to platform features like model versioning, safe deployment pipelines, latency-aware routing, and model health monitoring
• Collaborate with peers across ML, infra, and product teams to define architectural patterns and operational practices that support high availability and low latency at scale
• Guide decisions on model serving architecture using tools like vLLM, ONNX Runtime, and container orchestration in Kubernetes

• 8+ years of engineering experience in backend systems, ML infrastructure, or scalable platform development
• Expertise in serving embedding models in production environments
• Strong systems skills in languages like Go, Rust, C++, or Python, and experience profiling and optimizing performance
• Comfortable working on cloud-native distributed systems, with a focus on latency, availability, and observability
• Familiarity with inference runtimes and vector search systems (e.g., Faiss, HNSW, ScaNN)
• Proven ability to collaborate across disciplines and experience levels, from ML researchers to junior engineers
• Experience with high-scale SaaS infrastructure, particularly in multi-tenant environments

• Prior experience working with model teams on inference-optimized architectures
• Background in hybrid retrieval, prompt-based pipelines, or retrieval-augmented generation (RAG)
• Contributions to relevant open-source ML serving or vector search infrastructure

• Be part of shaping the future of AI-native developer experiences on the world’s most popular developer data platform
• Collaborate with ML experts from Voyage.ai to bring cutting-edge research into production at scale
• Solve hard problems in real-time inference, model serving, and semantic retrieval — in a system used by thousands of customers worldwide
• Work in a culture that values mentorship, autonomy, and strong technical craft
• Competitive compensation, equity, and career growth in a hands-on technical leadership role

• Contribute to the design and implementation of customer facing features and products using client and server side Typescript, React, Next.js, and Java
• Own project-level software delivery, including setting and meeting milestones and stakeholder communication
• Participate in product and architecture discussions
• Perform code reviews with peers and make recommendations on how to improve our code and software development processes
• Collaborate with other teams including cloud services, database, enterprise tools, drivers and support to coordinate changes or contribute to their projects
• Work with a talented team of engineers who care deeply about customer problems and implementing great software solutions with the best technologies for the job

• 7+ years of professional experience with application development
• Proficiency in modern programming languages and techniques; preferably Typescript (client and server) and Java
• Proficiency in modern Javascript frameworks; preferably React and Next.js
• Experience developing distributed systems, cloud services and SaaS products
• A history of effectively working with and building consensus among stakeholders across job functions (e.g., product and design)

As a Staff Engineer on the Atlas Data Federation and Archiving team, you will lead the design, optimization, and scalability of our storage and federated query systems. This role focuses on high-performance distributed storage, data lifecycle management, and efficient data retrieval at scale.

This role can be based in New York City, Austin, San Francisco, Seattle, or remotely in the United States.

Storage & Data Processing Performance

• Architect and optimize large-scale storage solutions for federated data access, ensuring efficient retrieval, indexing, and query performance
• Optimize data archival pipelines for high-throughput ingestion, durability, and cost-efficiency
• Improve data tiering and lifecycle policies for moving and querying data efficiently across hot, warm, and cold storage tiers
• Reduce operational costs through intelligent storage layout, compaction strategies, and query execution optimizations

Distributed Query & Execution Engine

• Improve and scale our distributed query execution engine, optimizing it for multi-source federated queries and data lake processing
• Enhance query performance across object storage (e.g., S3, GCS, Azure Blob) by optimizing indexing, partitioning, and compaction techniques
• Implement workload-aware autoscaling for query execution and data processing
• Reduce incident rates by improving system resilience, failover mechanisms, and observability

Technical Leadership & Mentorship

• Guide architectural decisions and lead design reviews across engineering teams
• Mentor engineers in distributed systems, data storage optimization, and operational excellence
• Partner with Product Management to define the technical roadmap for storage and data federation solutions
• Participate in on-call rotation, providing senior oversight for incident response and postmortem retrospectives

• 10+ years experience in software engineering, with a focus on backend and distributed storage systems
• Expertise in large-scale storage systems, such as distributed databases, cloud object storage (S3, Azure Blob, GCS), or data lake technologies (Iceberg, Delta Lake, Hudi, etc.)
• Strong background in designing and optimizing storage layers, indexing, and data lifecycle management
• Experience optimizing query engines for high-volume, low-latency federated data access
• Track record of improving system reliability, observability, and cost-efficiency
• Experience with Kubernetes-based deployment of distributed storage or query systems
• Proficiency in Go or Java (preferred, but not required)
• Deep understanding of query optimizers, storage formats (Parquet, ORC), and indexing strategies
• Experience with disaggregated storage and cloud-native data lake solutions
• Proven ability to lead technical initiatives as an individual contributor while mentoring senior engineers and driving technical excellence within a team.

Our team is building a cloud-based distributed system responsible for the core components of search including data ingestion, performance, query language, query execution, for both relevance-based search and vector search. Our product is being adopted quickly and there are many interesting projects. This is a technical role where you will be responsible for the success of complex Search Query feature development.

This role is based in our San Francisco office.

• Lead complex projects across the MongoDB ecosystem, for instance, co-optimization of approximate nearest neighbor vector search with the MongoDB aggregation framework
• Set project level strategy, architect features, and lead projects to successful execution
• Identify, design, and implement features enhancing our query language, performance, and operability
• Perform code reviews with peers and make recommendations on how to improve our software development processes
• Influence and grow team members through active mentoring and leading by example

• 5+ years experience in data management systems, ideally with a strong query processing and optimization background
• Experienced in developing stateful distributed systems
• Experienced with designing high-volume query engines, such as a database, search system, or vector search system
• Experienced in the development and maintenance of stateful distributed systems
• Eager to shape the technological direction of a complex system and have the ability to lead initiatives through collaboration with others
• Experienced in debugging and profiling multithreaded JVM applications

• In 1 month, you will be mostly learning - becoming familiar with our teams, systems and architecture
• In 3 months, you'll contribute to the improvement of our search engine service
• In 6 months, you'll be designing new features and collaborating with other teams from across the company on projects related to search query optimization and execution

• Design, architect, build, and deliver core pieces of Atlas Online Archive, primarily coding in Java, Go & Javascript (React)
• Approach problems from a customer-focused perspective and use data to propose high ROI improvements to our product and operations
• Collaborate with colleagues and other teams in Atlas to scope, spec and deliver new features
• Define your top priorities and focuses, communicate them, and execute against them

• 3+ years experience in software engineering, primarily focused on cloud-native backend systems
• B.Sc / M.Sc in Computer Science or similar field, or equivalent practical experience
• Proficient in a modern compiled programming language (Go, Java, C#, C++, etc.)
• Experience implementing, and operating distributed storage systems with high availability and durability guarantees
• Enjoys working with others towards a common goal
• You're a self-starter! You're comfortable organizing your own time and propose changes backed by data
• Is curious, collaborative, empathetic, coachable and intellectually honest
• Great diagnostics skills, considering edge cases in designs with a bias for simplicity
• Has a passion for problem solving and learning new things in the domains of computer science and software engineering

• Experience building and testing full-stack systems (using React or similar frameworks)
• Oncall experience and having customer first approach when considering different approaches
• Experience designing Data Lake protocols (and similar e.g. Delta Lake, Hudi,...)
• Experience optimizing throughput in distributed storage systems
• Experience working with multiple cloud providers (AWS, GCP, Azure)

• In 1 month, you'll learn about the system and ask quality questions, go through the inner & outer loop
• In 3 months, you'll have contributed to a large project
• In 6 months, you’ll be leading the design and execution of your own project

This role is based in the SF Bay Area - hybrid work model.

• Design and contribute to our integrated search platform to support ML workloads.
• Participate in product and architecture discussions
• Perform code reviews with peers and make recommendations on how to improve our code and software development processes
• Collaborate with other teams including cloud services, database, enterprise tools, drivers and support to coordinate changes or contribute to their projects

• 3+ years experience delivering large scale systems on production.
• Proficient in modern programming languages and techniques preferably Java and Python.
• Experienced in developing distributed systems, cloud services and SaaS products
• Experience with building Gen AI applications or tools like langchain, llama index is preferred but not required.

• In three months, you will contribute to search repos to build the foundation for embedding management in MongoDB as well as develop relationships with your team and leaders on other teams.
• In six months, you will have delivered a key feature and contribute to new ML capabilities in Atlas Search.
• In twelve months, you'll be building POCs, designing new features, and collaborating with other Cloud and Server teams on complex projects.