Research Engineer, Security and Privacy, DeepMind

At Google, research-focused Software Engineers are embedded throughout the company, allowing them to setup large-scale tests and deploy promising ideas quickly and broadly. Ideas may come from internal projects as well as from collaborations with research programs at partner universities and technical institutes all over the world.

From creating experiments and prototyping implementations to designing new architectures, engineers work on real-world problems including artificial intelligence, data mining, natural language processing, hardware and software performance analysis, improving compilers for mobile platforms, as well as core search and much more. But you stay connected to your research roots as an active contributor to the wider research community by partnering with universities and publishing papers.

The frontier of agentic capabilities is defined by empirical limits; we push models there systematically. As a core engine of the Gemini development cycle, we leverage automated red-teaming to expose sophisticated adversarial vulnerabilities and elucidate distinct failure modes. Our evaluations power the primary leaderboards that model training climbs against. By rigorously measuring failing modes, we directly shape defensive mitigations and steer the broader optimization space.

Artificial intelligence will be one of humanity’s most transformative inventions. At Google DeepMind, we are a pioneering AI lab with exceptional interdisciplinary teams focused on advancing AI development to solve complex global challenges and accelerate high-quality product innovation for billions of users. We use our technologies for widespread public benefit and scientific discovery, ensuring safety and ethics are always our highest priority.

We are pushing the boundaries across multiple domains. Our global teams offer diverse learning opportunities and varied career pathways for those driven to achieve exceptional results through collective effort.

The US base salary range for this full-time position is $174,000-$252,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.

Responsibilities

• Build robust pipelines, benchmarks, and auto-red teaming tools to measure model vulnerabilities and adversarial robustness.
• Collaborate closely with post-training teams to turn your evaluation insights and discovered failure modes into actionable model upgrades.
• Generalize your solutions into reusable evaluation libraries and frameworks for protecting agents across Google, and sharing your knowledge through publications, open source, and education.

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
• 5 years of experience with software development in one or more programming languages.
• 3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.
• 1 year of experience in evaluating, stress-testing, or red-teaming generative models to understand their capabilities, limitations, and security flaws.

Preferred qualifications:

• Master's degree or PhD in Computer Science or related technical field.
• Experience or publications in ML security, adversarial robustness, privacy, safety, or alignment.
• Research experience or publications in ML security, adversarial robustness, privacy, safety, or alignment.
• Ability to leverage Python and frameworks like JAX or PyTorch to build robust evaluation systems, adapting internal tooling shifts.
• Ability to autonomously translate abstract adversarial research into concrete benchmarks.
• Ability to drive dissect frontier models.

Note: By applying to this position you will have an opportunity to share your preferred working location from the following: New York, NY, USA; Mountain View, CA, USA.