Research Developer, Frontier AI Incubation, DeepMind – Google DeepMind – Montreal, QC

Google DeepMind is one of the world’s leading AI research labs, and this Research Developer role sits right at the frontier of what’s possible. Based in Montreal, QC, you’ll be embedded within the Frontier AI Incubation team — a group pushing the boundaries of foundational AI systems across multiple domains, from personalized model adaptation to multimodal reasoning and agentic workflows.

This isn’t a role where you’re maintaining existing pipelines. You’ll be doing end-to-end technical development — designing algorithms, running experiments, building production-grade architectures, and partnering directly with Google product and development teams to bring cutting-edge AI capabilities into real-world environments. The work spans research and product, and you’ll be expected to move fluidly between both.

About the Role: Research Developer, Frontier AI Incubation

At the core of this position is the design, training, and optimization of foundational algorithms and machine learning systems. That includes working on personalized model adaptation, contextual memory architectures, dynamic prompt optimization, and multimodal reasoning. You’ll lead technical development from early algorithmic design through to production-grade architecture and scaled serving infrastructure, ensuring that promising research ideas can be deployed broadly and quickly.

Collaboration is central to how this team operates. You’ll partner directly with Google development and product teams to integrate core technologies into production environments — including Project Helix, agent workspaces, and intelligent system integrations. You’ll also be responsible for formulating novel evaluation methodologies, both automated and human-in-the-loop, to measure capability gains, latency and compute efficiency, alignment, and personalization fidelity.

Benefits and Salary

This position offers a salary range of $185,000 – $190,000 CAD per year, plus a 15% bonus target, equity, and a full benefits package. Google provides a comprehensive benefits programme — more details are available on Google’s careers benefits page.

Responsibilities

In this role, you’ll be operating across the full technical stack — from early-stage algorithm design through to scaled deployment. The work demands both rigorous scientific thinking and strong engineering execution, and your contributions will directly shape how Google DeepMind’s frontier AI systems reach production and users at scale.

• Design, train, and optimize foundational algorithms and ML systems, including 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 through to production-grade architecture and scaled serving infrastructure
• Partner directly with Google development and product teams to integrate and harden core technologies within production environments, including Project Helix, agent workspaces, and intelligent system integrations
• Formulate novel evaluation methodologies, both automated and human-in-the-loop, to measure capability gains, latency and compute efficiency, alignment, and personalization fidelity

Requirements / Skills

Google DeepMind is looking for someone who combines a strong foundation in computer science or machine learning with hands-on experience building and deploying ML systems. The ideal candidate is equally comfortable in a research context and a production engineering environment, and can navigate both with confidence.

• 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 programming experience in Python or C++
• 1 year of experience taking technical projects or ML systems from conceptual formulation through to implementation and deployment
• Experience with foundation model development, including techniques such as reinforcement learning from human/AI feedback, supervised fine-tuning, parameter-efficient tuning, or inference optimization (preferred)
• Experience or interest in personalization, adaptive systems, user modelling, retrieval-augmented generation, or agentic memory architectures (preferred)
• Experience with ML frameworks and large-scale model training or serving infrastructure (preferred)
• Experience collaborating across research and product boundaries to co-design technical architectures (preferred)
• Strong scientific analysis, technical problem-solving, and communication skills (preferred)

How to Apply

To apply, visit the official Google Careers posting using the link below. Make sure your resume is up to date and highlights your most relevant ML and systems experience before submitting.

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