Principal Research Software Engineer

Build robust solutions spanning product experiences, Application Programming Interfaces (APIs), data pipelines, model integration layers, and cloud infrastructure that connect research prototypes to real product environments. Provide technical direction, mentor engineers, and guide architecture and design decisions, upholding best practices for code quality, security, privacy, testing, observability, and reproducibility. Help shape the team's engineering culture, long-term strategy, and adoption of modern AI-driven engineering practices. Bachelor's Degree in Computer Science or related technical field AND 6+ years technical engineering experience with coding in languages including, but not limited to, Python, Rust, C++, C#, C, Java, JavaScript Master's Degree or Ph.D. in Computer Science, Operations Research, Applied Mathematics, or a related field AND 10+ years of technical engineering experience OR Bachelor's Degree AND 12+ years of equivalent experience OR equivalent experience. Experience applying Artificial Intelligence (AI) or Machine Learning (ML) to real systems, products, or engineering workflows, with working knowledge of how modern models are trained, adapted, evaluated, optimized, or deployed, and the curiosity and ability to learn quickly across unfamiliar domains. Familiarity with several of the following: agentic AI frameworks, tool use and function calling, prompt engineering, retrieval-augmented generation, evaluation frameworks, model fine-tuning or post-training, observability, and AI-assisted code generation. Demonstrated engineering fundamentals and broad technical range, with experience designing, building, and shipping software systems, taking ambiguous, early-stage ideas from prototypes through production across layers such as user-facing experiences, Application Programming Interfaces (APIs) and services, data workflows, cloud systems, and model integration. Experience providing technical leadership across cross-functional efforts—defining direction, mentoring engineers, and driving execution on complex projects—and building systems that balance speed of experimentation with production requirements such as reliability, security, privacy, and maintainability. Proficiency with a major cloud and AI platform stack; experience with Azure, Azure AI services, and Copilot-based tools. Contributions to research papers, patents, or open-source projects at the intersection of engineering and research. Design and integrate agentic AI frameworks and LLM-based pipelines into research tools and engineering workflows, building systems that coordinate AI agents for complex tasks such as code generation, evaluation, debugging, and refinement. Develop tool-use and function-calling architectures that enable AI systems to interact with codebases, APIs, and data sources. Apply modern AI engineering practices—including prompt design, retrieval-augmented generation, and evaluation frameworks. Contribute to model experimentation and adaptation, such as fine-tuning, where appropriate to the project.