Lead Machine Learning Engineer / Applied Scientist

About the Role

Upwork Inc.’s (Nasdaq: UPWK) family of companies connects businesses with global, AI-enabled talent across every contingent work type including freelance, fractional, and payrolled. This portfolio includes the Upwork Marketplace, which connects businesses with on-demand access to highly skilled talent across the globe, and Lifted, which provides a purpose-built solution for enterprise organizations to source, contract, manage, and pay talent across the full spectrum of contingent work. From Fortune 100 enterprises to entrepreneurs, businesses rely on Upwork Inc. to find and hire expert talent, leverage AI-powered work solutions, and drive business transformation. With access to professionals spanning more than 10,000 skills across AI & machine learning, software development, sales & marketing, customer support, finance & accounting, and more, the Upwork family of companies enables businesses of all sizes to scale, innovate, and transform their workforces for the age of AI and beyond.

We’re looking for a Lead Machine Learning Engineer / Scientist to join our Algorithms and Research team within the ML & AI organization. In this role, you will help shape the reinforcement learning systems that power high-impact experiences across Upwork, including Search & Recommendations and Uma, our AI assistant. You will design and scale advanced reasoning, planning, and retrieval systems that connect research innovation to production outcomes. This is a hands-on, high-ownership role for someone excited to push the frontier of RL, autonomous agents, and applied machine learning on a fast-evolving platform.

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.

What It Takes to Catch Our Eye

• 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.