Research Engineer, Astra, 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.

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.

Minimum qualifications:

• Bachelor's degree in Computer Science, Machine Learning, a related technical field, or equivalent practical experience.
• 2 years of experience conducting research or engineering systems.
• 2 years of experience building in Python.
• 2 years of experience with agentic AI workflows, frameworks, and approaches.

Preferred qualifications:

• Master's degree or PhD in Computer Science, Machine Learning, or a related field.
• Experience with data collection, model fine-tuning, and evaluation.
• Experience with inference optimization techniques or extensive prompt tuning.
• Track record of publication at leading conferences on the topic of agentic AI and multimodal LLMs.

Responsibilities:

• Contribute to building a full-stack Universal Assistant prototype across key workstreams, including agent infrastructure, multi-agent systems, and full-stack integration.
• Guide model inference, optimization, fine-tuning, and evaluation to improve system capabilities and reliability.
• Support data workflows through synthetic data generation and prompt optimizations.
• Collaborate with partner teams across DeepMind, including the Gemini and Gemini App teams, to integrate and evaluate new agentic capabilities.