Upwork Inc.
Technology
LeadMachineLearningEngineer/AppliedScientist
Neural analysis suggests this role is
optimal for Lead candidates.
“Lead Machine Learning Engineer / Applied Scientist at Upwork Inc.. Skills: Reinforcement learning, Applied machine learning, Autonomous agents. Design and advance reinforcement learning systems. Build scalable retrieval and decisioning architectures”
What You'll Achieve.
Connect research innovation to production outcomes; Deliver high-impact outcomes
Industry & Context.
Reasoning; Planning; Decision making
3 days in office, Initially employed through a partner
What They're Looking For.
Must Have
Proven experience designing, training, and deploying reinforcement learning systems in production, Deep familiarity in planning methods such as Monte Carlo Tree Search and policy or value-based approaches, Expertise in machine learning systems that use vector databases, graph databases, knowledge graphs, or graph neural networks, Track record of leading technically complex initiatives across research and engineering partners, Experience applying AI tools and iterative prompt or workflow strategies to accelerate model development, analysis, debugging, or experimentation, Passion for building intelligent agent systems that combine reinforcement learning, large language models, and retrieval techniques
Nice to Have
Transition to employment with Upwork may be possible
What You'll Do.
Design and advance reinforcement learning systems
Build scalable retrieval and decisioning architectures
Combine structured and unstructured data
Lead cross-functional efforts to move ML and RL
Partner closely with engineering
Improve explainability
Evaluate emerging techniques in reinforcement learning
Translate promising innovations into practical applications
Mentor engineers and scientists
Deliver high-impact outcomes aligned with organizational goals
and momentum across complex cross-functional
How You'll Work.
Team & Collaboration
Cross-functional efforts; Engineering, research, and Trust & Safety teams; Cross-functional initiatives
Process & Methodology
Roadmap planning
Full Job Description
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 and learn more about Lifted at Go-Lifted and follow on LinkedIn. 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 obs
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