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

DeepMind, Google’s pioneering AI research lab, is looking for a Research Developer to join its Frontier AI Incubation team in Montreal, Quebec. This is a rare chance to work at the cutting edge of artificial intelligence — shaping the foundational systems that will power intelligent products for billions of users worldwide.

In this role, you’ll sit at the intersection of research and engineering, designing and deploying sophisticated machine learning systems, from personalized model adaptation to agentic memory architectures. You won’t just prototype ideas — you’ll take them all the way from concept to production-grade infrastructure.

About the Role: Research Developer, Frontier AI Incubation

As a Research Developer embedded within DeepMind’s Frontier AI Incubation team, you’ll lead the technical development of advanced ML algorithms and foundational AI systems. Your work will span experimental prototyping, architectural design, and scaled serving infrastructure — collaborating directly with Google product and development teams to bring core technologies into live production environments such as Project Helix, agent workspaces, and intelligent system integrations.

You’ll also play a key role in formulating evaluation methodologies — both automated and human-in-the-loop — to rigorously measure capability gains, latency and compute efficiency, alignment, and personalization fidelity. Collaboration across research and product boundaries is central to this position, as is contributing to the broader scientific community.

Benefits and Salary

This position offers a salary range of $185,000 to $190,000 CAD, plus a 15% bonus target, equity, and a comprehensive benefits package. Google’s benefits are well-regarded and include a wide range of perks — visit Google’s careers page to learn more about what’s included.

Responsibilities

Day-to-day, you’ll be driving the full lifecycle of AI system development — from early-stage algorithmic design through to scaled deployment. These responsibilities sit at the frontier of what’s possible in modern AI engineering, and they require both deep technical rigour and a collaborative mindset.

• 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 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 (e.g., Project Helix, agent workspaces, intelligent system integrations)
• Formulate evaluation methodologies — both automated and human-in-the-loop — to measure capability gains, latency and compute efficiency, alignment, and personalization fidelity

Requirements / Skills

The ideal candidate brings strong academic foundations in computer science or mathematics, hands-on experience with machine learning systems, and the practical ability to move from research concept to deployed solution. DeepMind values scientific rigour alongside the engineering skill to build systems at scale.

• 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 models (preferred) — including fine-tuning, reinforcement learning from human/AI feedback, supervised fine-tuning, parameter-efficient tuning, or inference optimization
• Familiarity with personalization, RAG, agentic memory, or adaptive systems (preferred) is a strong asset
• Experience with ML frameworks and large-scale model training or serving infrastructure (preferred)
• Strong communication skills for scientific analysis and cross-functional collaboration across research and product teams

How to Apply

To apply, visit the official job posting using the link below. Make sure your resume is up to date before submitting your application.

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