About Our Client
Are you an exceptional ML Scientist eager for real-world impact and to push AI's boundaries? Our Toronto based client is looking to add to their industry leading team, an ML Research Scientist who is ready to dive into diverse, cutting-edge AI problems across multiple high-growth companies.
Responsibilities
As an ML Research Scientist on our R&D team, you'll be at the forefront, acting as a force multiplier for our companies. You'll bring cutting-edge machine learning expertise directly to their most pressing challenges, helping them build and scale transformative AI products. Beyond our portfolio, you'll contribute to our investment process technical diligence, ensuring we back the most innovative and technically sound companies.
• Hands-on R&D: Rapidly prototype, run ablation studies, publish internal tech notes and
occasional external blog posts or papers.
• Co-create AI roadmaps with founders: Run discovery workshops, frame measurable
wins and pitch 12–18-month AI strategies to CEOs and boards.
• Safety & governance: Ship privacy guardrails, evaluation pipelines and alignment tests
to keep models reliable and compliant.
• Architect & ship intelligent AI systems: Build production-ready LLM agents,
Retrieval-Augmented Generation (RAG) pipelines and fine-tuned domain-specific
models; orchestrate workflows with frameworks like LangChain/LlamaIndex/PydanticAI,
optimise inference and cost, and deploy on AWS/GCP with robust MLOps.
• Executive storytelling: Convert deep-tech concepts into strategic outcomes; negotiate
budgets and timelines with C-suite stakeholders.
Qualifications
• Education & Experience: A Ph.D. or Master's degree in Computer Science, Electrical Engineering, Statistics, or a related quantitative discipline with a focus on machine learning, optimization theory, or related areas.
• Hands-On Expertise: 3+ years of practical experience working with deep learning toolkits such as Scikit-learn, TensorFlow, or PyTorch. Deep Grasp of GenAI, LLM tooling and Context Engineering is a must.
• Strong Software Development: Solid proficiency in Python and excellent software development skills, enabling you to write clean, efficient, and maintainable code.
• Problem Formulation & Communication: The ability to pragmatically formulate an applied research problem, design experiments, implement solutions, and clearly communicate findings to diverse audiences with varying technical backgrounds.
• Project Leadership & Collaboration: Demonstrated ability to lead various projects, collaborate effectively with external groups (like startup companies), and
manage expectations of both internal and external
stakeholders.
• Experience contributing to open-source AI/ML-related projects or publishing research papers at top-tier conferences.
• Familiarity with cloud platforms (AWS, GCP, Azure) for MLOps and model deployment.
