Senior Lead Machine Learning Engineer - Search & Recommendations

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.

We’re looking for a Senior Lead Machine Learning Engineer to build, ship, and own core Search and Recommendation systems that directly impact relevance, engagement, and hiring outcomes across the marketplace.

This role is for someone who is deeply hands-on and thrives on turning messy problems into working systems. You’ll work end-to-end on retrieval, ranking, and personalization, partnering closely with product and engineering to take ideas from whiteboard to production, and then iterate until the metrics move.

You won’t just advise on models - you’ll design them, implement them, deploy them, and hold yourself accountable for their impact.

Responsibilities:

• Own and build Search and Recommendation models end-to-end, from problem definition to production rollout.
• Design and implement retrieval and ranking pipelines that improve relevance, engagement, and downstream hiring outcomes.
• Develop personalization and behavioral features that capture user intent, preferences, and interaction patterns.
• Train and iterate on ranking and re-ranking models, optimizing for both offline relevance metrics (e.g., NDCG, MRR) and online A/B results.
• Work deeply in production systems, balancing model quality, latency, scalability, and reliability.
• Partner closely with product, data, and platform teams to translate ambiguous goals into concrete ML solutions.
• Instrument, debug, and improve models in production using logs, metrics, and experiments.
• Contribute code across the stack where needed—feature pipelines, training jobs, inference services.
• Provide technical leadership by setting the bar for quality, ownership, and execution within the Search & Recs team.

What it takes to catch our eye:

• Strong hands-on experience building Search or Recommendation systems in production with measurable impact.
• Deep understanding of retrieval and ranking systems, including candidate generation, re-ranking, and evaluation.
• Experience developing personalization models using behavioral and contextual signals.
• Solid grounding in ML fundamentals and the engineering required to run models at scale.
• Comfort owning ambiguous problems: you can define success metrics, design experiments, and drive execution.
• Proven ability to ship, iterate, and improve systems - not just prototype them.
• High ownership mindset: you treat relevance and quality as your responsibility, not someone else’s.
• Bonus: experience running large-scale online experiments or improving marketplace/search metrics.

Come change how the world works.

At Upwork, you’ll shape the future of work for a global, remote-first workforce, creating economic opportunities for professionals worldwide. While we have a physical office in Palo Alto, we currently hire full-time employees in 21 U.S. states, making it easier than ever to join our mission from wherever you call home.

Our culture is built on trust, risk-taking, customer focus, and excellence, all in service of our core mission: to create economic opportunities so people have better lives. We embrace authenticity and inclusion, encouraging everyone to bring their whole selves to work. Personal and professional growth is a priority here, supported through development programs, mentorship, and our Upwork Belonging Communities.

We’re proud to offer benefits that go beyond the basics, including comprehensive medical coverage for you and your family, unlimited PTO, a 401(k) plan with matching, 12 weeks of paid parental leave, and an Employee Stock Purchase Plan. Visit our Life at Upwork page to learn more about our values, working principles, and the overall employee experience.

Ready to help shape the future of work? Check out our Careers page and follow us on LinkedIn, Facebook, Instagram, TikTok, and X to learn more about life at Upwork.

Upwork is an Equal Opportunity Employer committed to recruiting and retaining a diverse and inclusive workforce. We do not discriminate based on race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, genetic information, or other legally protected characteristics under federal, state, or local law.

Please note that a criminal background check may be required once a conditional job offer is made. Qualified applicants with arrest or conviction records will be considered in accordance with applicable law, including the California Fair Chance Act and local Fair Chance ordinances.

Client-provided location(s): Flexible / Remote, Palo Alto, CA

Job ID: 7490995003

Employment Type: OTHER

Posted: 2025-10-09T23:32:57