Software Engineer, Code Generation

MongoDB is bolstering its hiring, focusing on creating tools that guide customers in transitioning their applications from relational databases to MongoDB. As businesses evolve their application development frameworks, they're increasingly drawn to the versatility of the document model. The Relational Migrator team, already instrumental in this area, aids developers in making the shift from relational databases to MongoDB. Now, they're broadening their toolkit and are keen on refining code using a mix of AI and traditional text processing. MongoDB is seeking a Software Engineer with solid software engineering skills and a machine learning background. Joining this team, you'll be pivotal in a product engineering group dedicated to helping users navigate code conversion challenges with AI's support. This role will be based out of North America in the PST and MST zones only. The ideal candidate for this role will have - 2+ years of professional software development experience in Java or another programming language - Experience with generative AI and specifically LLMs is highly desirable - Experience with text processing engines such as ANTLR is highly desirable - Strong understanding of software engineering, system design, data engineering and/or cloud architecture - Have experience with compiler design, code parsing or related areas - Familiarity with concepts like abstract syntax trees (AST), lexical analysis, and syntax analysis - Curiosity, a positive attitude, and a drive to continue learning - Actively engages in emerging trends and research relevant to product features - Excellent verbal and written communication skills Position Expectations - Collaborate with stakeholders to define and implement a code modernisation strategy, ensuring that transformed code aligns with modern software practices while preserving original functionality - Develop and maintain a robust code parser to accurately interpret legacy code structures, converting them into a standardised format like an abstract syntax tree (AST) - Provide thought leadership to the engineering team on using emerging technologies, frameworks and approaches to solve different problems - Collaborate closely with product managers and other engineers to understand business priorities and propose new solutions - Contribute and maintain the high quality of the codebase with tests that provide a high level of functional coverage and non-functional aspects with load testing, unit testing, integration testing, etc - Share your knowledge by giving brown bags, tech talks, and evangelising appropriate tech and engineering best practices - Define and improve business & product metrics to optimise the quality and cost of AI usage Success Measures Within the first three months, you will have: - Familiarise yourself with the MongoDB database and aggregation language - Familiarise yourself with the problem space and the domain - Set up software development infrastructure (tech stack, build tools, etc) to enable development using the relevant tech stacks - Started collaborating with your peers and contributed to code reviews Within six months, you will have: - Worked on and delivered a large-scale AI-based feature in the product - Contributed to and helped deliver a few releases of the product - Reviewed and contributed to scope and technical design documents Within 12 months, you will have: - Delivered large-scale features across our entire tech stack - Helped recruit and interview new members of the team - Collaborated with other teams at MongoDB About MongoDB MongoDB is built for change, empowering our customers and our people to innovate at the speed of the market. We have redefined the data platform for the AI era, enabling builders to create, transform, and disrupt industries with software. MongoDB’s unified data platform, the most widely available, globally distributed data platform on the market, helps organizations modernize legacy workloads, embrace innovation, and unleash AI. Our cloud-native platform, MongoDB Atlas, is the only globally distributed, multi-cloud data platform and is available across AWS, Google Cloud, and Microsoft Azure. With offices worldwide and over 67,000 customers, including 75% of the Fortune 100 and AI-native startups, relying on MongoDB for their most important applications, we’re powering the next era of software. Our compass at MongoDB is our Leadership Commitment, guiding how and why we make decisions, show up for each other, and win. It’s what makes us MongoDB. To drive the personal growth and business impact of our employees, we’re committed to developing a supportive and enriching culture for everyone. From employee affinity groups, to fertility assistance and a generous parental leave policy, we value our employees’ wellbeing and want to support them along every step of their professional and personal journeys. Learn more about what it’s like to work at MongoDB, and help us m

2+ years of professional software development experience in Java or another programming language. Experience with generative AI and specifically LLMs is highly desirable. Experience with text processing engines such as ANTLR is highly desirable. Strong understanding of software engineering, system design, data engineering and/or cloud architecture. Have experience with compiler design, code parsing or related areas. Familiarity with concepts like abstract syntax trees (AST), lexical analysis, and syntax analysis. Curiosity, a positive attitude, and a drive to continue learning. Actively engages in emerging trends and research relevant to product features. Excellent verbal and written communication skills.

Collaborate with stakeholders to define and implement a code modernisation strategy, ensuring that transformed code aligns with modern software practices while preserving original functionality. Develop and maintain a robust code parser to accurately interpret legacy code structures, converting them into a standardised format like an abstract syntax tree (AST). Provide thought leadership to the engineering team on using emerging technologies, frameworks and approaches to solve different problems. Collaborate closely with product managers and other engineers to understand business priorities and propose new solutions. Contribute and maintain the high quality of the codebase with tests that provide a high level of functional coverage and non-functional aspects with load testing, unit testing, integration testing, etc. Share your knowledge by giving brown bags, tech talks, and evangelising appropriate tech and engineering best practices. Define and improve business & product metrics to optimise the quality and cost of AI usage.