This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Staff AI/ML Engineer based in Canada.
This role sits at the core of building next-generation AI systems that power real-world operational decision-making at scale. You will design and ship production-grade AI solutions that go far beyond experimentation, focusing on systems that automate complex workflows and continuously improve through real outcomes. The work spans both intelligent agentic systems and applied machine learning models that drive forecasting, optimization, and decision intelligence. You will operate in a high-ownership environment where speed, rigor, and reliability are essential, and where AI is embedded directly into production workflows. Working closely with founders and cross-functional teams, you will help define what AI-native infrastructure looks like in a high-stakes logistics environment. This is a hands-on, deeply technical role for someone excited to turn cutting-edge AI into measurable business impact.
Accountabilities:
Design, build, and deploy production-grade AI systems that combine multi-agent orchestration, decision intelligence, and automation to replace and enhance complex operational workflows.
- Develop agentic AI systems capable of end-to-end task execution, including routing, tool use, and workflow automation across operational domains such as support, onboarding, and exceptions handling.
- Build applied machine learning models for forecasting, prediction, matching, ranking, and optimization that directly inform product and business decisions.
- Design and implement continuous learning systems, including data pipelines, feedback loops, and evaluation frameworks to improve model performance over time.
- Own AI system reliability through robust evaluation frameworks, observability, tracing, guardrails, and safe deployment practices such as shadow testing.
- Define when to use models, agents, or rule-based systems, making pragmatic build-vs-buy and build-vs-rule decisions.
- Collaborate closely with product and engineering teams to bring AI prototypes into scalable, production-ready systems.
- Mentor engineers and contribute to raising the technical bar across AI development and system design practices.
Requirements:
This role requires a highly hands-on AI/ML engineer with proven experience building and scaling real-world AI systems in production environments.
- Demonstrated experience shipping production AI/ML systems, not just prototypes, with attention to latency, cost, reliability, and edge cases.
- Strong expertise in either generative/agentic AI systems (multi-agent workflows, tool use, RAG, structured outputs, orchestration frameworks like LangGraph or LangChain) or applied machine learning for decision intelligence (forecasting, ranking, optimization, matching).
- Solid Python engineering skills and experience building production APIs and services (e.g., FastAPI) in modern distributed systems.
- Experience designing evaluation frameworks, including offline/online testing, A/B testing, drift detection, and safe rollout strategies.
- Strong understanding of ML system design, including data pipelines, feature engineering, and production monitoring.
- Ability to operate in high-stakes, operational environments where system reliability directly impacts business outcomes.
- Strong communication skills and ability to lead technical initiatives with autonomy and clarity.
- Bonus: Experience in logistics, marketplaces, optimization/operations research, real-time systems, or agent-based automation.
- Bonus: Exposure to reinforcement learning, bandits, multimodal systems, or large-scale MLOps platforms.
Benefits:
- Competitive compensation including salary, equity, and performance-based bonuses.
- Fully remote work environment with flexibility across the United States and Canada.
- Comprehensive medical, dental, and vision insurance coverage.
- Equity participation in a high-growth, venture-backed organization.
- Opportunity to work on cutting-edge AI systems with real-world operational impact.
- Regular offsites and opportunities for in-person collaboration.
- Fast-paced, high-ownership culture focused on shipping, learning, and continuous improvement.
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