JOB DESCRIPTION:
About Us
Artificial Intelligence could be one of humanity’s most useful inventions. At Google DeepMind, we’re a team of scientists, engineers, machine learning experts and more, working together to advance the state of the art in artificial intelligence.
The Role
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.
Key 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.
About You
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.
In order to set you up for success as a Research Engineer at Google DeepMind, we look for the following skills and experience:
- 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
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