Staff Engineer jobs at Mongodb in United States, Palo Alto

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

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.

• 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

• 5+ 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

• 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

• Be part of building the AI foundation of the world’s most popular developer data platform
• Collaborate with ML researchers from Voyage.ai to bring novel ideas into scalable systems
• Tackle challenging problems in inference, observability, and distributed infrastructure
• Work in a culture that emphasizes mentorship, ownership, and technical excellence

• 9+ years experience working in a Customer Success, Account Management, Client Services or other similarly customer-centric role
• A background and passion for advocating on behalf of your customers - this role should act as an extension of our customers team within MongoDB
• A mind for technology - we’ll teach you about MongoDB and databases, but our customers and product are inherently technical and you should have an aptitude and curiosity to learn about those concepts
• The ability to act with a high level of autonomy, you will be expected to take full ownership of your customer portfolio and make key decisions to drive effective customer outcomes; including deciding when to include our executive team or c-suite in customer escalations, how to de-risk or de-escalate customer issues and conflict and advocating to internal teams including sales, professional services, etc
• An entrepreneurial mindset - this is a new team at MongoDB where you will be required to both deviate from established procedure and often build entirely new process or practices to ensure we best serve this customer segment
• Team player and passion for collaboration - this role will work with some of our most strategic growth customers so must align closely to Sales, Professional Services, Tech Services, and the broader MDB ecosystem
• Prior exposure to database, cloud, and infrastructure technology is a plus
• German speaking

• Work as a strategic advisor to your customer providing them with guidance on MongoDB best practices and their overall technology strategy; this could include running enablement sessions alone or with another internal MongoDB team members, advising the customer on strategies to optimize their technical environment or current spend with MongoDB, positioning and recommending product features and best practices to accelerate customers time to value and growth
• Collect feedback and identify roadblocks from customers to inform internal teams including Product, Professional Services, and Leadership on how MongoDB can build a stronger product and go to market organization
• Act as the link between our customers and product engineering to develop new innovative solutions. You will be key in building the future roadmap of our product by acting as the Product team’s eyes and ears in this field
• De-escalate and resolve critical customer issues and complaints by finding the best possible solution for both the customer and MongoDB; this could include anything from navigating a customer outage that has a financial impact on their business, to helping an application team devise a custom MongoDB solution or implementation for their critical application, no day is the same
• Build and execute account plans to mitigate risk and drive growth 3+ quarters out across your portfolio
• Lead in-person executive business reviews for strategic customers in your portfolio, including interfacing with C-suite executives and other technical leaders to align to business objectives and agree to a mutual success plan
• Work on strategic internal projects to help build the Customer Success program; our expectation is that anyone in this role has strong business acumen and the ability to create and teach best practices, new process, and enablement to the broader organization
• Document all customer interactions in internal systems, including Gainsight and Salesforce.com
• Provide feedback and guidance to leadership on key signals within MongoDB Atlas that indicate healthy or unhealthy customer accounts; as our product evolves, we’ll need to be constantly adjusting our engagement strategies based on these signals, which you’ll be in the best position to identify and share back with internal teams
• Manage the relationship with Sales Leadership and Account Executives in your territory, including reporting on business performance, training on best practices, and rolling out program updates to ensure sales people and leaders are abreast of best practices for interacting with Customer Success
• Forecast expected churn and growth to your senior leadership team
• Help interview, onboard and ramp new team members - as a more senior team member you will have an immediate role in who joins the team
• Act as a leader amongst your peers, running enablement sessions, product certifications and being vocal in team meetings to ensure those around you grow

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

• Design and contribute to our integrated search platform to support ML workloads
• Participate in product and architecture discussions
• Provide thought leadership to continue to scale our architecture to evolve for the needs of tomorrow
• 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

• 10+ years experience delivering large scale systems on production
• Proficient in modern programming languages and techniques preferably Java and Python
• Eager to impact and envision the technological future of a complex system
• 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
• Experienced in designing and maintaining search platforms or distributed databases is preferred but not required
• Experienced in debugging and profiling multithreaded JVM applications and distributed applications 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 design for 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

This role can be based out of our NYC office or remotely from anywhere in North America.

• Minimum 12 years of experience in programming, debugging, and performance tuning distributed and/or highly concurrent C/C++ software systems
• Strong systems fundamentals, including multi-threaded programming, performance profiling, and expert-level network programming
• Familiarity with database internals or building core components for data processing systems
• Familiarity with observability ecosystem and best practice
• Excellent verbal and written technical communication skills and a strong desire to collaborate with colleagues and mentor engineers
• Excellent time and project management skills including the ability to make realistic assessments of project cost and complexity

• Lead the architecture and implementation of low-level networking components that drive the performance, scalability, cost-efficiency, and resiliency of MongoDB Server
• Design and implement observability improvements that enable MongoDB engineers and customers to quickly and accurately diagnose the root cause of production issues
• Handle networking-related customer escalations from Technical Support team and coach teammates to do the same
• Write production-ready database code in C++, improve the existing code, and mentor their team to write higher quality code
• Own all code the Networking & Observability Team maintains, ensuring it achieves a high standard for quality (including security, durability, availability, and performance) and maintainability
• Write and improve unit and integration tests in C++, Javascript, and Python to demonstrate correctness
• Diagnose test failures, identify bugs in existing code, fix them, and prevent bugs from being introduced in new code
• Investigate the performance impact of code changes that may cause software performance regressions
• Interview candidates for advanced software engineering positions
• Develop and maintain expertise on cutting edge database, networking, and observability developments from industry and academia
• Lead development and project management of some of the largest projects across the company
• Collaborate with stakeholders and engineering teams across the company to jointly work on large initiatives
• Advise Product Management on technical product direction, engineering complexity and inter-project dependencies
• Collaborate with Product Management and Engineering leadership to define product roadmaps

• In the first month, you will have understood the high level architecture of MongoDB networking and observability, and fixed a few bugs
• In three months, you will have contributed to the development in C++ of a project slated for the next major release of MongoDB, and diagnosed and fixed a few customer or testing-reported issues
• In six months, you will have taken on code review responsibilities and are involved in reviewing the design for new features
• In twelve months, you will be leading the development of new features and initiatives, helping to mentor engineers on the team, and using your experience to drive the long-term technical roadmap of the Networking & Observability Team

This role can be based out of our New York City office or remotely in the North America region.

• Minimum 12 years of experience in programming, debugging, and performance tuning distributed and/or highly concurrent C/C++/Rust software systems. Experience in C++ is preferable, but not required
• Strong systems fundamentals, including multi-threaded programming and performance profiling
• Familiarity implementing distributed systems such as consensus protocols, logical clocks, distributed transactions, and fault tolerance
• Experience implementing database internals or building core components for data processing systems
• Excellent verbal and written technical communication skills and a strong desire to collaborate with colleagues and mentor engineers
• Excellent time and project management skills including the ability to make realistic assessments of project cost and complexity

• Architect and develop novel protocols for moving data between different shards in a sharded database cluster, ensuring correctness, performance, resiliency, and low-cluster impact
• Design and implement protocols for placing data intelligently throughout a cluster and the plans for how to achieve that placement
• Handle distributed systems related customer escalations from Technical Support team and coach teammates to do the same
• Write production-ready database code in C++, improve the existing code, and mentor their team to write higher quality code
• Own all code the Cluster Scalability Team maintains, ensuring it achieves a high standard for quality (including security, durability, availability, and performance) and maintainability
• Write and improve unit and integration tests in C++, Javascript, and Python to demonstrate correctness
• Diagnose test failures, identify bugs in existing code, fix them, and prevent bugs from being introduced in new code
• Investigate the performance impact of code changes that may cause software performance regressions
• Interview candidates for advanced software engineering positions
• Develop and maintain expertise on cutting edge database and distributed systems research from industry and academia
• Lead development and project management of some of the largest projects across the company
• Collaborate with stakeholders and engineering teams across the company to jointly work on large initiatives
• Advise Product Management on technical product direction, engineering complexity and inter-project dependencies
• Collaborate with Product Management and Engineering leadership to define product roadmaps

• In the first month, you will have understood the high level architecture of MongoDB sharding and fixed a few bugs
• In three months, you will have contributed to the development in C++ of a project slated for the next major release of MongoDB, and diagnosed and fixed a few customer or testing-reported issues
• In six months, you will have taken on code review responsibilities and are involved in reviewing the design for new features
• In twelve months, you will be leading the development of new features and initiatives, helping to mentor engineers on the team, and using your experience to drive the long-term technical roadmap of the Cluster Scalability Team