Research Engineer, Post-Training for Code Security Analysis
About The Position
In this role, you'll work with a team of elite researchers and engineers to design and implement post-training strategies that enhance Gemini’s capabilities in code security analysis. You will bring contributions to our ML innovation, post-training refinement (SFT/RLHF), advanced evaluation, and data generation to ensure our models can reliably perform safe and powerful code security analysis. We are seeking individuals who excel in fast-pacing environments and are eager to contribute to the advancement of AI. We highly value the ability to invent novel solutions to complex problems, embracing a can-do and fail-fast mindset. We are looking for someone who genuinely believes in the future of AI and is committed to devoting their energy in this field.
Requirements
- BSc, MSc or PhD/DPhil degree in computer science, stats, machine learning or similar experience working in industry
- Deep understanding of statistics is strongly preferred
- Experiences in fine-tuning and adaptation of LLMs (e.g. advanced prompting, supervised fine-tuning, RLHF)
- Strong knowledge of systems design and data structures
- Proven experience with TensorFlow, JAX, PyTorch, or similar leading deep learning frameworks
- Recent experience conducting applied research to improve the quality and training/serving efficiency of large transformer-based models
- A passion for Artificial Intelligence.
- Excellent communication skills and proven interpersonal skills, with a track record of effective collaboration with cross-functional teams
Responsibilities
- Design and Implement advanced post-training algorithms (SFT, RLHF, RLAIF) to optimize Gemini for code security tasks and secure coding practices.
- Diagnose and interpret training outcomes (regressions in coding ability, gains in security reasoning), and propose solutions to improve model capabilities.
- Actively monitor and evolve the system's performance through metric design.
- Develop reliable automated evaluation pipelines for code security that are strongly correlated with human security expert judgment.
- Construct complex benchmarks to probe the limits of the model’s ability to reason about control flow, memory safety, and software weakness.
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Education Level
Ph.D. or professional degree
Number of Employees
1,001-5,000 employees
