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Montreal Institute for Learning Algorithms

MILA is the machine learning laboratory at the University of Montreal.

Program Manager - Founders in Residence

Full-TimeUpdated on 10/17/2026
No salary listed
Junior
Montreal, QC, Canada
Hybrid
Project & Program ManagementProgram Management

About the job

Requirements
  • A few years of experience and readiness to own a program rather than support one.
  • Credibility with CEOs of venture-capital-backed companies; this is non-negotiable.
  • High energy, drive, sociability, and mission orientation.
Responsibilities
  • Identify, recruit, and onboard experienced entrepreneurs and operators for the Founders in Residence bench.
  • Build relationships within the Mila research community so professors and researchers regard the program as a serious path.
  • Run the Founders in Residence selection committee process from initial conversation through signed match.
  • Represent the program credibly and energetically with CEOs of venture-capital-backed companies.
  • Own and improve program metrics, including signings, matches made, and conversion rate.
  • Partner with the Program Lead to redesign talent sourcing and qualification and launch initiatives to grow the program, including geographical expansion, Applied AI, and reverse brain drain.
Desired Qualifications
  • Experience as a consultant at a top firm with exposure to technology or startups.

About the company

M

Montreal Institute for Learning Algorithms

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Website

Montreal Institute for Learning Algorithms is an artificial intelligence research institute affiliated with Quebec universities. The company conducts machine-learning research, trains students and researchers, publishes scientific work, and collaborates with industry and public organizations. It serves researchers, students, universities, companies, governments, and the broader AI community. Its operating model centers on an academic research community supported by scientific programs, partnerships, computing, and institutional operations. Teams work across machine-learning research, software, education, partnerships, policy, communications, and administration.

Company Size

501-1,000

Company Stage

Grant

Total Funding

$5.8M

Headquarters

Montreal, Canada

Founded

1993

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Simplify's Take

What believers are saying

  • Quebec granted Mila 36 million dollars on February 27, 2026 for talent and research.
  • Mila and Mozilla launched open-source AI partnership on March 26, 2026.
  • Torc Robotics partnered with Mila in 2026, validating its physical-AI research.

What critics are saying

  • Canada's CIFAR AI Chairs program expires after March 31, 2026, threatening hiring.
  • Mila depends heavily on Quebec support, risking political budget shifts in 2027.
  • If startup conversion stalls, Mila becomes a government-funded lab with no self-sustaining relevance.

What makes Montreal Institute for Learning Algorithms unique

  • Yoshua Bengio founded Mila, anchoring elite deep-learning credibility and global recruiting power.
  • Mila's 1,500-plus researchers and students create unmatched Montreal AI density.
  • Mila integrates university research with Inovia's Venture Scientist Fund, accelerating spinouts.

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Benefits

  • Health Insurance
  • Dental Insurance
  • Disability Insurance
  • Life Insurance
  • Paid Vacation
  • 401(k) Retirement Plan
  • Remote Work Options
  • Flexible Work Hours

Growth & Insights and Company News

Headcount

6 month growth

16%

1 year growth

17%

2 year growth

17%
Business Wire
May 26th, 2026
Torc Robotics becomes first autonomous trucking firm to partner with Mila AI Institute

Torc Robotics has partnered with Mila – Quebec Artificial Intelligence Institute to advance physical AI research for autonomous trucking. The collaboration makes Torc the only autonomous trucking company within Mila's ecosystem, providing access to researchers, students and dedicated research space in Montreal. The partnership will focus on generative world models, multi-agent behaviour modelling, reinforcement learning and foundation models for physical AI systems. Torc aims to bridge simulation and real-world performance to develop safer, more efficient autonomous transportation. Mila, founded by Professor Yoshua Bengio, is the world's largest academic AI research centre specialising in deep learning, with over 1,500 members. The collaboration builds on Torc's existing Montreal presence and affiliation with Mila dating back to 2020.

Startup Researcher
Jan 24th, 2026
Mila and Inovia launch $100M fund to keep Canadian AI talent and startups at home

Mila, Montreal's AI research institute, has partnered with Inovia Capital to launch the Venture Scientist Fund, a $100 million initiative aimed at commercialising Canadian AI research. The fund addresses a stark imbalance: Canada hosts 10% of the world's top AI researchers but attracts less than 2% of global AI venture capital. The fund will invest in over 55 AI-native companies, focusing on foundational AI models, deep tech and next-generation computational infrastructure. It is integrated with Canada's national AI institutes, including Mila, Alberta's Amii and Toronto's Vector Institute, creating a pipeline from academic research to commercial ventures. The initiative tackles an acute pre-seed and seed funding gap, with nearly 70% of Canadian-led startups currently headquartered outside the country, representing significant economic value loss.

PYMNTS
Jun 3rd, 2025
Ai Pioneer Yoshua Bengio Launches Nonprofit To Develop Safe Ai

An artificial intelligence (AI) pioneer who has been vocal about the risks of AI has launched a nonprofit focused on developing safe AI systems. Yoshua Bengio — who won the Turing award along with Nobel laureate Geoffrey Hinton and Meta Chief AI Scientist Yann LeCun — on Tuesday (June 3) unveiled LawZero, which is an AI [] The post AI Pioneer Yoshua Bengio Launches Nonprofit to Develop Safe AI appeared first on PYMNTS.com.

PR Newswire
Jun 3rd, 2025
Yoshua Bengio Launches Lawzero: A New Nonprofit Advancing Safe-By-Design Ai

MONTRÉAL, June 3, 2025 /PRNewswire/ - Yoshua Bengio, the most-cited artificial intelligence (AI) researcher in the world and A.M. Turing Award winner, today announced the launch of LawZero , a new nonprofit organization committed to advancing research and developing technical solutions for safe-by-design AI systems.LawZero is assembling a world-class team of AI researchers who are building the next generation of AI systems in an environment dedicated to prioritizing safety over commercial imperatives. The organization was founded in response to evidence that today's frontier AI models are developing dangerous capabilities and behaviours, including deception, self-preservation, and goal misalignment. LawZero's work will help to unlock the immense potential of AI in ways that reduce the likelihood of a range of known dangers associated with today's systems, including algorithmic bias, intentional misuse, and loss of human control.LawZero is structured as a nonprofit organization to ensure it is insulated from market and government pressures, which risk compromising AI safety. The organization is also pulling together a seasoned leadership team to drive this ambitious mission forward."LawZero is the result of the new scientific direction I undertook in 2023, after recognizing the rapid progress made by private labs toward Artificial General Intelligence and beyond, as well as its profound implications for humanity," said Yoshua Bengio, President and Scientific Director at LawZero. "Current frontier systems are already showing signs of self-preservation and deceptive behaviours, and this will only accelerate as their capabilities and degree of agency increase

VentureBeat
Apr 23rd, 2025
More Accurate Coding: Researchers Adapt Sequential Monte Carlo For Ai-Generated Code

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More. Coding with the help of AI models continues to gain popularity, but many have highlighted issues that arise when developers rely on coding assistants. However, researchers from MIT, McGill University, ETH Zurich, Johns Hopkins University, Yale and the Mila-Quebec Artificial Intelligence Institute have developed a new method for ensuring that AI-generated codes are more accurate and useful. This method spans various programming languages and instructs the large language model (LLM) to adhere to the rules of each language.The group found that by adapting new sampling methods, AI models can be guided to follow programming language rules and even enhance the performance of small language models (SLMs), which are typically used for code generation, surpassing that of large language models.In the paper, the researchers used Sequential Monte Carlo (SMC) to “tackle a number of challenging semantic parsing problems, guiding generation with incremental static and dynamic analysis.” Sequential Monte Carlo refers to a family of algorithms that help figure out solutions to filtering problems. João Loula, co-lead writer of the paper, said in an interview with MIT’s campus paper that the method “could improve programming assistants, AI-powered data analysis and scientific discovery tools.” It can also cut compute costs and be more efficient than reranking methods. The researchers noted that AI-generated code can be powerful, but it can also often lead to code that disregards the semantic rules of programming languages. Other methods to prevent this can distort models or are too time-consuming. Their method makes the LLM adhere to programming language rules by discarding code outputs that may not work early in the process and “allocate efforts towards outputs that more most likely to be valid and accurate.”Adapting SMC to code generationThe researchers developed an architecture that brings SMC to code generation “under diverse syntactic and semantic constraints.” “Unlike many previous frameworks for constrained decoding, our algorithm can integrate constraints that cannot be incrementally evaluated over the entire token vocabulary, as well as constraints that can only be evaluated at irregular intervals during generation,” the researchers said in the paper. Key features of adapting SMC sampling to model generation include proposal distribution where the token-by-token sampling is guided by cheap constraints, important weights that correct for biases and resampling which reallocates compute effort towards partial generations.The researchers noted that while SMC can guide models towards more correct and useful code, they acknowledged that the method may have some problems.“While importance sampling addresses several shortcomings of local decoding, it too suffers from a major weakness: weight corrections and expensive potentials are not integrated until after a complete sequence has been generated from the proposal. This is even though critical information about whether a sequence can satisfy a constraint is often available much earlier and can be used to avoid large amounts of unnecessary computation,” they said. Model testingTo prove their theory, Loula and his team ran experiments to see if using SMC to engineer more accurate code works. These experiments were: