About Skyfall
We're building something the enterprise world doesn't have yet: an Enterprise World Model.
While the rest of the field races to scale LLMs as the primary path to intelligence, we see a more principled opportunity in a hybrid architecture by combining the semantic richness of LLMs with the structural reasoning capabilities of world models. As such, we are developing Enterprise World Models (EWMs) that can understand, simulate, and act across complex enterprise systems.
This isn't our first time building something that matters. Skyfall was founded by the original Maluuba team, pioneers of the deep learning revolution, who worked closely with leaders such as Yoshua Bengio and Richard Sutton, before Maluuba’s $160M acquisition by Microsoft, where it became Microsoft’s AI research center in Canada. We've built companies before. We've made research breakthroughs before. Now we're doing it again, this time with a clear target: the next $5B enterprise AI company.
We're VC-backed, moving fast, and assembling a world-class team to match our ambitions.
We're not building another AI wrapper instead we are building the next foundation layer for enterprise AI.
Role Overview
As a Frontier Model Research Manager, you'll lead a team of expert researchers and engineers who are trying to understand at a deep level, how modern large language models and world models work internally.
Few things can accelerate this work more than great managers. Your work as manager will be critical in making sure that our fast-growing team is able to meet its ambitious enterprise world models research goals over the coming years. In this role, you will work and guide our researchers closely, lead to drive the team's success, translating cutting-edge research ideas into tangible goals and overseeing their execution. You will manage team execution, careers and performance, facilitate relationships within and across teams, and drive the hiring pipeline.
You will be the connective tissue between research ambition and execution, ensuring the team ships high-quality work, stays aligned on priorities, and continues to push the state of the art in world modeling and enterprise AI.
This is a rare opportunity to shape foundational AI research at a company with deep roots in the field.
This role reports directly to the CEO at Skyfall and is a hybrid position based out of our Toronto office.
Responsibilities
Lead, mentor, and grow a team of Research scientists and Research Engineers, helping them do the best work of their careers
Ensure the team executes against Skyfall's research agenda with speed, quality, and scientific rigor
Remove blockers, improve workflows, and build the processes that keep a fast-moving research team operating at its best
Foster a culture of rigorous experimentation, intellectual curiosity, and thoughtful risk-taking
Drive the team's recruiting pipeline, from supporting sourcing and interviewing to closing world-class research talent
Provide regular feedback, performance management, and clear paths for career growth
Act as the connective tissue between research, engineering, and business teams, ensuring alignment and unblocking dependencies
Communicate research progress and results clearly to leadership and cross-functional stakeholders
Stay current with the latest advancements in LLMs, world models, and causal reasoning to help the team stay sharp and informed
Requirements
Minimum Qualifications
3 to 5 years of proven experience as a people manager of technical research or ML teams
Experience in agentic AI systems, including computer use agents (CUA), multi-step reasoning, and tool-use frameworks
Familiarity with the full LLM development lifecycle, from pre-training and mid-training through post-training alignment and evaluation
Ability to move fluidly between research depth and organizational leadership without losing stride
Strong communication skills, with the ability to explain complex trade-offs to both technical and non-technical audiences
Preferred Qualifications
PhD in Machine Learning, AI, or a related field
Experience with simulation-based learning, world models, or multi-agent systems
Strong publication record or demonstrable research impact in machine learning, AI, or related fields
Actively involved in the research community through publications, conferences, or open-source contributions
Prior experience at a frontier AI lab, large tech or enterprise software company, research-driven startup, or top-tier academic institution
Comfortable operating in an early-stage, fast-moving environment where ambiguity is the norm