About the Role
The role involves conducting foundational research to improve machine learning applications in programming languages and software engineering tools, aiming to publish findings and develop practical solutions.
Responsibilities
- Design and execute experiments in machine learning for code
- Develop models that understand and generate programming language constructs
- Collaborate with engineers to integrate research into real-world tools
- Publish results in top-tier academic venues
- Identify new research directions in AI for software development
- Evaluate model performance using rigorous scientific methods
- Contribute to open-source projects when applicable
- Mentor junior researchers and interns
- Stay current with advancements in deep learning and programming languages
- Translate theoretical insights into prototype implementations
- Work closely with product teams to assess research applicability
- Define metrics for evaluating code-related AI systems
- Explore multimodal learning approaches involving code and text
- Investigate scalability of models on large codebases
- Analyze limitations and biases in code-trained models
- Participate in peer reviews and technical discussions
- Present findings internally and at conferences
- Ensure reproducibility of research outcomes
- Collaborate across disciplines to solve complex problems
- Balance long-term research with practical constraints
Nice to Have
- Postdoctoral research experience
- Industry research experience in AI labs
- Contributions to major open-source machine learning projects
- Experience mentoring research teams
- Work involving large code corpora
- Publications in programming languages or software engineering venues
- Hands-on experience with transformer architectures
- Knowledge of formal methods in software verification
- Familiarity with cloud computing platforms
- Experience deploying models in production environments
Compensation
Competitive salary and benefits package
Work Arrangement
Hybrid or remote options available
Team
Collaborative research team focused on long-term AI advancements in software development
Research Focus
The team explores machine learning applications that enhance how developers interact with code, including code completion, bug prediction, and program synthesis. Research emphasizes generalization, interpretability, and real-world impact.
Impact
Findings contribute to both academic knowledge and practical tools used by millions of developers worldwide. Projects often bridge theoretical advances with user-centric design.
Available for qualified candidates
