Adam Salvail
Manager, Applied AI at Mila - Quebec Artificial Intelligence Institute
Montreal, Quebec, Canada
Summary
👤
Senior🎓
Top SchoolAdam Salvail is a manager of applied AI with 11 years of experience turning research-grade methods—non-linear optimization, deep learning and time series forecasting—into production value for enterprises and advertisers. He has led cross-functional teams at Meta, ServiceNow and Element AI to mature early AI tech, ship forecasting and experimentation platforms, and scale products used by top advertisers. Comfortable bridging research and product, he built playbooks for tech transfer, R&D governance and innovation pipelines that survived an acquisition and enterprise rollout. An early contributor to Theano-era tooling (Blocks) and a lifelong automation enthusiast, he combines hands-on model work with strategic program leadership. Based in Montreal, he focuses on decision-making under uncertainty and making incremental research outcomes measurable and operational.
11 years of coding experience
6 years of employment as a software developer
Master's degree, Computer Science, Master's degree, Computer Science at Université de SherbrookeDEC, Accounting and Business/Management, DEC, Accounting and Business/Management at Cégep de Drummondville
Github Skills (9)
neural-network10
machine-learning10
blocks10
activation-function10
python10
theano10
recurrent-neural-networks9
testing8
documentation7
Programming languages (10)
Github contributions (5)
mila-iqia/blocks
Aug 2015 - Aug 2015
A Theano framework for building and training neural networks
Role in this project:
ML Engineer
ML EngineerContributions:7 commits, 2 PRs, 12 comments in 13 days
Contributions summary:Adam primarily contributed to the implementation and testing of activation functions within the Blocks framework, a Theano-based library for neural networks. Their work included adding a `Softplus` brick, documenting its functionality, and integrating activation tests (Softplus, Softmax, Logistic) to ensure proper behavior. Further contributions involved refining the documentation for LSTM and other recurrent network components, clarifying input arguments and improving clarity.
pytorchdeep-learningtheanoneural-networksmachine-learning
ASalvail/smartlearner
Jun 2015 - May 2016
Contributions:2 PRs, 68 pushes, 25 branches in 10 months
pythonkeeprequiredata-sciencedeep-learning
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