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
Are you passionate about using machine learning to drive meaningful change? As a Senior Applied Machine Learning Engineer at Upwork, you will leverage advanced machine learning techniques to deliver cutting-edge solutions that directly impact our global platform and user experience. You’ll work with large-scale data to solve complex problems, from improving search relevance to personalizing recommendations, and create a powerful, seamless experience for our users. This is a unique opportunity to contribute to the innovation at the world’s largest work marketplace, where your work will enable our community of freelancers and clients to connect and collaborate in impactful ways.
Responsibilities- Analyze millions of structured and unstructured data to identify patterns and insights in user behavior and build meaningful features to improve model performance
- Design and implement efficient and reusable features, models and systems for different machine learning applications (classical & deep learned models) in low latency fashion
- Contribute to the performance and continued optimization of our recommendation systems: build machine learning models to improve understanding of user preferences, user intent and context to deliver accurate, relevant and personalized recommendations
- collaborate with the business, analytics, and engineering counterparts to share the discovered data stories with stats, charts, and formal presentations, and finally propose recommendations to maximize the business impact.
- Build and fine‑tune large language models (LLMs), transformers, agents, and/or hybrid systems to improve relevance, semantic understanding, and user experience.
- Masters/PhD in Computer Science, Machine Learning, NLP, Mathematics, or a related quantitative field (or equivalent research experience).
- 3+ years of relevant industry experience building large scale ML systems
- Prior experience with large language models, retrieval augmented generation (RAG), or agentic AI.
- Strong experience with Python, SQL PyTorch and/or TensorFlow
- Clear track record of publications in peer‑reviewed conferences or journals (e.g. ACL, EMNLP, NeurIPS, ICML, ICLR, KDD, SIGIR, WWW etc.).
- Knowledge of distributed training techniques, and efficiency at scale (memory, latency, hardware constraints).
This position will initially be employed through a partner to ensure a seamless hiring process while we establish the hub. Once the hub is established, there may be opportunities to transition to employment with Upwork depending on business needs and other requirements. While employed by the partner, you’ll work as part of Upwork’s team, with access to our resources, culture, and growth opportunities.
To learn more about how Upwork processes and protects your personal information as part of the application process, please review our Global Job Applicant Privacy Notice
