Staff Engineer, Code Generation
MongoDBUnited StatesPosted 16 April 2026
Job Description
The Code Gen team is tasked with building AI-powered code transformation tools that transform rigid, legacy applications that suffer from poor scalability and high operating costs into modern, microservices-based architectures that are built on top of MongoDB. Join our team and be at the forefront of innovation and creativity.
We are looking for a Staff Engineer with domain expertise and years of experience in modernizing legacy applications that are based on traditional database systems. A significant advantage is profound prior experience in leveraging AI, particularly LLMs and GenAI capabilities, to enable reliable, self-driving automation of the code transformation, iterative build, and test processes. In this role, you will be instrumental in initiating technical strategies and ideas, lead the Code Gen team in designing, building, and optimizing our code transformation workflow and tools. You will work on critical components that ensure the scalability, efficiency, and reliability of our services. This involves crafting sophisticated orchestration layers, robust integration points, and high-performance data systems that seamlessly connect and leverage advanced AI capabilities for code generation, build and test.
This role will be based out of North America.
A strong candidate for this position will have:
Extensive experience (8+ years) in software development and operations, with a proven track record of delivering high performance, correctness, and architectural excellence in fast-paced environments
Experience using Relational Databases such as Oracle, MySQL, Microsoft SQL Server or PostgreSQL
Experience with tools and methodologies for code analysis, refactoring, and automated testing
Experience in designing and implementing complex software systems, collaborating effectively with engineers of all experience levels to achieve high reliability and performance
Practical knowledge of integrating GenAI into large-scale, complex systems, including a clear understanding of appropriate use cases for GenAI
Excellent verbal and written technical communication skills and a strong desire to collaborate cross-functions
Experience in leading the design and development of complex workflow for AI model integration and output processing.
Excellent time and project management skills including the ability to make realistic assessments of project cost and complexity
Curiosity, a positive attitude, and a drive to continue learning, in particular building AI skillset
Position Expectations
Strategic Partnership: Partner with product managers, AI researchers and other engineers to translate complex requirements into robust technical solutions. Guiding the team through technical challenges related to autonomous code transformation
Architectural Leadership: Drive the architectural vision and technical strategy for code gen tools. Lead the team design and leverage GenAI to build reliable and scalable building blocks that are extendable and easy to assemble to an autonomous workflow
Engineering Excellence Mentorship: Define and champion engineering best practices, patterns, and standards across multiple teams. Act as a hands-on leader, contribute core, foundational code that solves the hardest problems and sets the standard for the rest of the team
Product Excellence: Collaborate with security and product teams to ensure the platform can execute code generation and LLM-based tasks securely, protecting both client data and MongoDB IP
Success Measures
Within the first three months, you will have:
Fully ramped up on our architecture, business domain, and AMP customers' pain points in modernizing legacy applications
Established yourself as the technical leader within the team, actively driving technical decisions
Begun collaborating with product counterparts and the code gen team to drive execution strategies on committed deliverables
Within six months, you will have:
Successfully led the team to deliver a prototype or initi ... (truncated, view full listing at source)
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