Senior Machine Learning Engineer - AI Innovation Teams

Senior Machine Learning Engineer – AI Innovation Teams

Location:
Toronto

Fitch Ratings is seeking a Senior Machine Learning Engineer to join our new AI Innovation teams in Toronto—where we're building the AI-powered future of financial analysis from the ground up. This isn't about tweaking hyperparameters or maintaining legacy models. This is about designing and shipping breakthrough generative AI systems, agentic workflows, and intelligent platforms that will transform how credit analysis happens and how global financial markets operate.

We're at a defining moment. Fitch is making a major strategic bet on AI, investing heavily in Toronto as our innovation center, and we're building teams of exceptional ML engineers to turn ambitious vision into production reality. As a Senior ML Engineer, you'll be a technical force on the team—building sophisticated ML systems, driving innovation through hands‑on work, mentoring engineers, and helping establish the technical standards that will scale across the organization.

You're joining early enough to shape how we build, with the resources and runway to do it right.

We need ML engineers who thrive on hard problems and greenfield opportunities—whether you're passionate about pushing the boundaries of what LLMs can do, excited to architect intelligent systems that reason and act, or energized by turning cutting‑edge research into production capabilities. If you're motivated by "let's build this and see what's possible" rather than "let's wait and see what others do," this is a high-impact role where you'll spend your time shipping transformative ML systems—working alongside talented engineers who are equally committed to building something significant.

• Build breakthrough ML systems with real impact – Design and ship production generative AI platforms, multi‑agent systems, and intelligent automation that will process billions in credit decisions; work on problems at the frontier of applied AI while seeing your work directly change how analysts and financial markets operate
• Work at the cutting edge of ML technology – Experiment with the latest LLMs and foundation models, implement novel RAG architectures, build agentic systems, fine‑tune neural networks, and leverage enterprise‑scale GPU clusters and cloud infrastructure; substantial conference and training budgets to stay at the forefront
• Technical leadership without the bureaucracy – Lead projects, mentor junior engineers, shape technical direction, and influence architectural decisions through your expertise and results—not through management hierarchy; your ideas and code will define how we build AI systems
• Toronto's world‑class AI ecosystem – Work in one of the world’s premier AI research hubs alongside Vector Institute researchers, attend cutting‑edge ML meetups and conferences, and be part of the community defining the future of applied AI and machine learning
• Greenfield innovation with enterprise backing – Build net‑new ML systems from scratch with the freedom to experiment boldly, fail fast, and push boundaries—backed by the compute resources, research budgets, and organizational support that most startups can only dream of
• Solve sophisticated ML challenges – Tackle hard problems at the intersection of NLP, document intelligence, reasoning systems, agentic workflows, and production‑scale deployment; work on challenges that will expand your expertise and push your technical boundaries
• Clear growth trajectory – High visibility to senior leadership, mentorship from experienced ML architects, and clear paths to Lead ML Engineer or Principal ML Engineer roles; build a reputation as a go‑to expert in generative AI and financial technology

• Design and build transformative ML systems – Lead the development of advanced generative AI solutions, agentic workflows, RAG architectures, and intelligent platforms using PyTorch, modern ML frameworks, and large language models; write production‑quality code that scales and performs
• Drive AI innovation through hands‑on technical work – Experiment with frontier models, implement novel ML architectures, evaluate emerging AI technologies, build…