The role
From Google's own posting.
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.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
Canada: $185000 - $190000 (CAD) + 15% bonus target + equity + benefits
Learn more about benefits at Google .
Design, train, and optimize foundational algorithms and ML systems (e.g., personalized model adaptation, agentic workflows, contextual memory architectures, dynamic prompt optimization, and multimodal reasoning).
Lead end-to-end technical development from algorithmic design and experimental prototyping to production-grade architecture and scaled serving infrastructure.
Partner directly with Google development and product teams to integrate and harden our core technologies within production environments (e.g., Project Helix, agent workspaces, and intelligent system integrations).
Formulate novel automated and human-in-the-loop evaluation methodologies to measure capability gains, latency/compute efficiency, alignment, and personalization fidelity.
Minimum qualifications:
Bachelor's degree in Computer Science, Machine Learning, Mathematics, Statistics, or equivalent practical experience.
2 years of experience in machine learning, algorithm design, data structures, or distributed software systems.
2 years of experience programming in Python or C++.
1 year of experience taking technical projects or machine learning systems from conceptual formulation to implementation and deployment.
Preferred qualifications:
Experience developing, fine-tuning, or optimizing foundation models including techniques such as reinforcement learning from human/AI feedback, supervised fine-tuning, parameter-efficient tuning, or inference optimization.
Experience or interest in personalization, adaptive systems, user modeling, retrieval-augmented generation, or agentic memory architectures.
Experience with machine learning frameworks and large-scale model training or serving infrastructure.
Experience collaborating across research and product boundaries to co-design technical architectures.
Experience in scientific analysis, technical problem-solving, and technical communication.
Bachelor's degree in Computer Science, Machine Learning, Mathematics, Statistics, or equivalent practical experience.
2 years of experience in machine learning, algorithm design, data structures, or distributed software systems.
2 years of experience programming in Python or C++.
1 year of experience taking technical projects or machine learning systems from conceptual formulation to implementation and deployment.