Senior AI / ML Research Engineer
Specializing in LLMs / VLMs / VLAs, Reinforcement Learning, and Embodied / Physical AI
I’m a senior AI/ML research engineer with 15+ years of experience building intelligent agents and AI systems that understand and reason about the world. My work spans foundation models, reinforcement learning, and physics-based simulation, continuously expanding into VLMs, VLAs, and embodied multimodal AI.
Currently part of the core research and engineering team at LawZero, a non-profit AI lab in Montreal led by Yoshua Bengio, I focus on advancing truthful, transparent, and safe-by-design AI through next-generation foundational models, and reasoning architectures.
My passion is right at the intersection of AI, simulation, and decision-making. I focus on:
- 🧠 Foundation Models & Alignment: LLM / VLM / VLA fine-tuning and alignment (SFT, DPO, RLVR/RLHF/RLAIF) with a focus on interpretable, safe, and truthful reasoning.
- 🧭 Reinforcement Learning: Online/offline, adversarial, multi-agent, imitation learning, and human-in-the-loop optimization.
- 🛰️ Embodied AI & High-Fidelity Simulation: Isaac Lab, Genesis, and physics-based virtual environments for grounded RL and world modeling.
- Experimenting with large-scale, distributed RL finetuning, using verifiable rewards for code-focused LLMs
- Building knowledge and hands-on experience on RL applied to foundation models
- Exploring the landscape around Vision-Language-Action models: SOTA architectures, training playbooks and recipes, benchmarks, data pipelines, and tooling
- Standardizing approaches and pipelines for DeepRL-based optimal decision making and control in real-world applications
I’m always happy to chat with people who share my interests and passions. Feel free to reach out!
- 🌐 Personal Website
- 🧠 Artificial Twin - The brand behind my AI/ML consultancy activity with the mission of supporting and advising companies building intelligent agents
Here are a few of my public repositories that reflect my work and interests:
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🐋 ContaineRL - Containerize your RL Environments and Agents Toolkit to package and deploy reinforcement learning environments and agents inside reproducible containers. |
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Reinforcement learning framework for decision-level interception prioritization of drone swarms. |
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A platform to train reinforcement learning agents in classic retro fighting games. |
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A library of reinforcement learning algorithms tailored for DIAMBRA Arena environments. |
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🔀 Extending GPU-Native RSL-RL Library Customized fork of RSL-RL library that supports multi-discrete action spaces. |