Lead Machine Learning Engineer / Applied Scientist

Upwork•Toronto, ON

8d•CA$179,000 - CA$302,500•Hybrid

About The Position

Upwork Inc. is seeking a Lead Machine Learning Engineer / Scientist to join their Algorithms and Research team within the ML & AI organization. This role will focus on shaping reinforcement learning systems that power key Upwork experiences like Search & Recommendations and the AI assistant, Uma. The position involves designing and scaling advanced reasoning, planning, and retrieval systems, bridging research innovation with production outcomes. It's a hands-on role for individuals passionate about advancing RL, autonomous agents, and applied machine learning on a dynamic platform.

Requirements

Proven experience designing, training, and deploying reinforcement learning systems in production, with deep familiarity in planning methods such as Monte Carlo Tree Search and policy or value-based approaches.
Strong expertise in machine learning systems that use vector databases, graph databases, knowledge graphs, or graph neural networks to improve reasoning and decision quality.
Track record of leading technically complex initiatives across research and engineering partners, with the judgment to balance experimentation, scalability, and production reliability.
Experience applying AI tools and iterative prompt or workflow strategies to accelerate model development, analysis, debugging, or experimentation while maintaining strong technical rigor.
Passion for building intelligent agent systems that combine reinforcement learning, large language models, and retrieval techniques to solve meaningful product and platform challenges.

Responsibilities

Design and advance reinforcement learning systems for reasoning and planning, including approaches inspired by Monte Carlo Tree Search, policy and value networks, and modern agentic decision-making methods.
Build scalable retrieval and decisioning architectures that combine structured and unstructured data, including vector search, knowledge graphs, and retrieval-augmented generation workflows.
Lead cross-functional efforts to move ML and RL models from research prototypes into reliable production systems with strong performance, robustness, and observability.
Partner closely with engineering, research, and Trust & Safety teams to improve explainability, interpretability, and risk mitigation across reinforcement learning and agent-based systems.
Evaluate emerging techniques in reinforcement learning, planning, and LLM-enabled systems, and translate promising innovations into practical applications for Upwork’s platform.
Mentor engineers and scientists through technical leadership, thoughtful code reviews, and strong software engineering practices that raise quality across the team.
Deliver high-impact outcomes aligned with organizational goals, while helping create clarity, structure, and momentum across complex cross-functional initiatives.