Role Description Search & Recommendations sits at the center of how work happens on Upwork. In this role, you will lead the engineering organization responsible for building the systems that connect millions of clients and freelancers across the globe. You will shape the future of Upwork’s matching platform by setting technical direction, scaling core systems, and delivering experiences that power everything from search and browse to conversational matching, agentic workflows, and emerging AI-native surfaces. This is a high-impact leadership opportunity to guide a strong engineering team while partnering closely with product, machine learning, and executive leadership on a platform central to Upwork’s long-term growth. Responsibilities - Define and drive the technical strategy for Upwork’s Search & Recommendations platform, aligning engineering investments with long-term business goals, marketplace performance, and platform scalability. - Lead, grow, and develop a high-performing organization of software and machine learning engineers, fostering a culture of ownership, inclusion, high standards, and continuous learning. - Deliver a unified Search & Recommendations platform that powers matching experiences across client and talent journeys, including search, recommendations, conversational experiences, and agentic workflows. - Modernize search infrastructure by guiding the migration from legacy systems to scalable, maintainable, and high-performance architectures with clear APIs, observable pipelines, and strong experimentation support. - Partner closely with product, ML, data science, design, and senior leadership to prioritize roadmaps, navigate tradeoffs, and translate complex platform decisions into measurable business outcomes. - Champion engineering excellence through strong practices in system reliability, latency, data integrity, responsible AI development, and operational health across critical marketplace systems. - Establish clear operating mechanisms, metrics, and team rhythms that improve execution, support long-range planning, and track platform health, engineering effectiveness, and business impact. Qualifications - Significant experience leading engineering teams in high-scale environments, with a strong track record of delivering search, recommendations, ranking, or retrieval systems that drive meaningful business results. - Deep technical fluency in distributed systems, data pipelines, search architecture, and modern machine learning infrastructure, with the ability to guide architecture decisions and mentor senior engineers and managers. - Proven success leading integrated teams of software engineers and machine learning engineers while building strong partnerships across product, data science, and executive stakeholders. - Ability to operate effectively at multiple altitudes, translating technical depth into strategic clarity and making sound decisions in ambiguous, fast-moving environments. - Strong applied understanding of AI-native engineering workflows, including using AI to accelerate design, experimentation, and delivery while ensuring technical accuracy, responsible use, and scalable team adoption. Benefits - Comprehensive medical coverage for you and your family. - Unlimited PTO. - 401(k) plan with matching. - 12 weeks of paid parental leave. - Employee Stock Purchase Plan. Company 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. 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.

Role Description The Buyer Growth team is responsible for driving Whatnot’s top-of-funnel growth and first-time buyer success. We're looking for a Machine Learning Engineer to accelerate buyer growth at Whatnot through intelligent, data-driven experiences. As the first ML engineer, you’ll build and own ML models personalization in onboarding and user targeting in our acquisition and engagement channels. This is a high-impact role on a team directly responsible for growing Whatnot’s buyer base and improving first-time user conversion. - Lead the design, development, and productionization of ML models that power user acquisition, activation, and retention (e.g., ranking models, targeting systems, uplift modeling). - Own the full model lifecycle: from data collection and feature engineering to model training, evaluation, deployment, and online experimentation. - Identify and prioritize high-impact opportunities where machine learning can unlock growth, collaborating with product, engineering, data, and marketing stakeholders. - Drive technical excellence and establish ML best practices across the Buyer Growth team. Qualifications - 5+ years of industry experience building and deploying ML systems at scale. - Demonstrated success in leading end-to-end ML projects that drove measurable business outcomes. - Deep expertise in personalization, ranking, or user modeling; strong intuition around user behavior in consumer products. - Advanced proficiency in Python, SQL, and common ML frameworks (e.g., PyTorch, TensorFlow, XGBoost). - Strong communication and leadership skills; ability to influence roadmap and align cross-functional teams. Requirements - Experience building ML applications in E-commerce or Social products. - Prior experience in growth domains: user segmentation, retention modeling, large scale recommendation systems, notification ranking, etc. Benefits - Flexible Time off Policy and Company-wide Holidays (including a spring and winter break). - Health Insurance options including Medical, Dental, Vision. - Work From Home Support. - Home office setup allowance. - Monthly allowance for cell phone and internet. - Care benefits. - Monthly allowance for wellness. - Annual allowance towards Childcare. - Lifetime benefit for family planning, such as adoption or fertility expenses. - Retirement; 401k offering for Traditional and Roth accounts in the US (employer match up to 4% of base salary) and Pension plans internationally. - Monthly allowance to dogfood the app. - Parental Leave: 16 weeks of paid parental leave + one month gradual return to work (company leave allowances run concurrently with country leave requirements which take precedence).

• Designing model evaluation pipelines for models in development and production • Designing user studies for subjective model evaluations. • Converting requirements into measurable metrics. • Designing and developing automated evaluation dashboard to see model performances and compare results. • Training new models to capture new and different evaluation metrics. • Communicating with the model team to help design better models based on the evaluation results. • Communicating with the data team to help decide the type of data necessary to improve model performance. • Communication with the product-manager to make sure product requirements are correctly measured. • Help grow the evaluation team as the founding member. • Lead the evaluation team in the future.

• Own ML Data Collection & Incubation Programs • Define and scope complex operational challenges into clear, actionable workstreams with measurable success criteria • Plan and execute data collection programs that feed Dandy's highest-priority ML initiatives • Monitor project health, measure operational efficiency, and flag implementation risks • Synthesize updates into clear status reports that drive alignment and keep projects on track • Build ML Workforce Enablement Programs • Design and lead training programs that upskill our manufacturing and DDC workforce on ML-related tools, workflows, and expectations • Own the full cycle from needs assessment through delivery and iteration, and adjust quickly when something isn't working • Stand Up Model Testing Infrastructure • Build the operational framework for testing new ML models in a live workforce environment • Define success criteria, coordinate across Operations and Quality teams, and create the reporting that makes model performance visible to decision-makers • Drive Stakeholder Alignment • Develop and maintain detailed project plans to monitor progress against milestones and deliverables • Build coalitions of support across teams including ML, Operations, Quality, and People • Communicate clearly across multiple levels and cultural contexts