Lead Machine Learning Engineer - Marketplace Matching & Optimization

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. Since its founding, Upwork Inc. has facilitated more than $30 billion in total transactions and services as it fulfills its purpose to create opportunity in every era of work. Learn more about the Upwork Marketplace at Upwork.com and follow us on LinkedIn , Facebook , Instagram , TikTok , and X ; and learn more about Lifted at Go-Lifted and follow on LinkedIn . At the heart of Upwork’s marketplace is a complex matching problem: connecting millions of clients with diverse, evolving needs to a global network of highly skilled talent. As a Lead ML Engineer in Marketplace Matching Optimization (under the Search Recommendations team) you will design and deliver the algorithmic systems that power this connection. This role sits at the intersection of machine learning, combinatorial optimization, and market design, with direct impact on marketplace outcomes such as client success, freelancer earnings, and long-term platform health. You will lead end-to-end development of matching and ranking systems, from problem formulation through production deployment. This includes advancing large-scale recommendation systems while exploring more explicit matching and optimization approaches over time. You will partner cross-functionally and help guide a small group of engineers and scientists, contributing both technically and through mentorship to raise the quality and impact of the team’s work. Responsibilities Design and implement large-scale matching and ranking systems that optimize marketplace outcomes such as client satisfaction, freelancer success, and platform liquidity Formulate marketplace matching problems using mathematical and algorithmic frameworks, translating them into production-ready systems Own the full lifecycle of algorithm development, including offline evaluation, experimentation, and A/B testing to measure real-world impact Develop and evolve feedback loops that define and improve match quality, incorporating signals into continuous model improvement Apply and extend techniques such as learning to rank, multi-objective optimization, and retrieval systems to improve matching performance Partner with product managers, applied scientists, and engineers to translate research into scalable, reliable systems Mentor and support engineers and researchers, contributing to technical direction and fostering high-quality, inclusive collaboration What it takes to catch our eye Experience building and scaling ranking, recommendation, or matching systems in production environments, ideally within a two-sided marketplace or similar platform Strong foundations in at least two of the following: machine learning, combinatorial optimization, statistics, or market design, with the ability to apply these concepts to real-world systems Demonstrated ability to take ambiguous, research-oriented problems and deliver measurable impact through production systems Proficiency in Python and modern ML frameworks such as PyTorch, TensorFlow, or JAX, with ... (truncated, view full listing at source)