Saris

Lead Machine Learning Engineer at Saris (Toronto, Canada)

Saris· Toronto, Canada·

Role details

Work type
Hybrid
Employment
Full-Time

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.

Application questions