
Lead Machine Learning Engineer at Saris (Toronto, Canada)
Saris· Toronto, Canada·
Role details
Job description
About Saris AI
We're a San Francisco, Montreal and Toronto based applied AI startup that's building the future of work in the banking industry. We are tackling a $100 billion/yr problem, doubling every quarter and pushing the boundaries of what’s possible with multi-turn AI agentic systems Our goal is to tackle the type of automation problems that require long-context reasoning, tool orchestration across legacy systems, and strict compliance loops: the ones without known answers. We’ve shipped real agents that handle real customer workflows in production. With a growing customer base and live deployments, we’re scaling up fast and looking for deeply technical builders who want to have outsized impact early. Our core engineering team is looking for a hands-on ML Engineering Lead who thrives in early-stage, ambiguous environments. You’ve led ML systems from v1 to scale, and enjoy defining both the technical direction and the systems that power them.
Your mission is to
- Own and lead the ML/AI function end-to-end, setting technical direction and standards across the company
- Architect and guide the development of multi-modal, agentic AI systems powering real-world workflows
- Define and oversee evaluation frameworks, datasets, and performance metrics to continuously improve agent quality
- Drive productionization of ML systems, ensuring reliability, scalability, and compliance in real-world environments
- Build and mentor a high-performing ML team over time, setting best practices across modeling, experimentation, and deployment
Who You Are
- 8+ years of experience in ML/AI engineering, including time as a technical lead or manager
- Proven track record of leading ML initiatives end-to-end, from problem definition → production deployment
- Deep experience with LLMs and/or agentic systems, ideally in real-world, customer-facing applications
- Strong understanding of ML fundamentals (deep learning, transformers, model evaluation, tradeoffs)
- Experience scaling ML systems in production, including monitoring, iteration, and reliability
- Demonstrated ability to lead engineers, influence architecture decisions, and drive technical direction
- Comfortable operating in early-stage, ambiguous environments with high ownership
- Strong communication skills with the ability to translate complex ML concepts into clear decisions
Bonus Points If You
- Have experience building agentic systems, orchestration layers, or long-context reasoning systems
- Are comfortable across the stack (data → modeling → infra → APIs)
- Have worked with both open-source and closed LLMs, including fine-tuning or retrieval systems (RAG)
- Have a strong product mindset and care deeply about real-world impact, not just model performance
Why Join Saris AI?
- 🏦 Join us in building the future of work for the trillion-dollar banking industry using cutting edge AI technology.
- ⚡Tackle ambiguous technical challenges with no clear answers.
- 💲Competitive compensation with premium benefits and equity package.
- 🤝Work with a stellar team of engineers, builders, and leaders; including repeat YC founders with a successful exit (Ready Education).
- 📈We already have production agents live with revenue-generating customers
- 🐦 🔥Our team is backed by Tier 1 Silicon Valley VCs
Why work at Saris
- Mission-driven: “Support the backbone of the US economy & community” – work that directly improves how financial institutions serve their communities.
- Culture highlights: “High performance + high care” – a high bar for output combined with mutual support. Operating principles include clarity, ownership, accountability, and customer obsession.
- Hybrid / remote flexibility: Employees work in a hybrid model (typical time on-site varies by role). Offices in San Francisco, Canada (multiple), and Qatar. Several roles listed as remote or in-office/remote.
- Engineering culture: Tackle complex AI agentic systems with long-context reasoning, multi-turn tool orchestration, and strict compliance loops. Small, fast-moving team (30 people) with significant ownership.
- Growth opportunity: Early-stage (Pre-Seed) with rapid headcount growth, offering potential for impact and career advancement.