Contract: Senior Machine Learning Engineer

Upwork ($UPWK) is the world’s work marketplace. We serve everyone from one-person startups to over 30% of the Fortune 100 with a powerful, trust-driven platform that enables companies and talent to work together in new ways that unlock their potential.  

Last year, more than $3.3 billion of work was done through Upwork by skilled professionals who are gaining more control by finding work they are passionate about and innovating their careers.  

This is an engagement through Upwork’s Hybrid Workforce Solutions (HWS) Team. Our Hybrid Workforce Solutions Team is a global group of professionals that supports Upwork’s business. Our HWS team members are located all over the world.

Work/Project Scope:

• Dive deep into a critical Trust & Safety problem area such as scam prevention, freelancer quality assessment, or agentic workflows. Work closely with key stakeholders to understand the problems and scope solutions.
• Build and iterate on machine learning models. Use detective skills to navigate the problem space and develop your solutions. Become a subject matter expert. Collaborate with cross-functional partners to test your solutions and gain feedback.
• Collaborate with engineering and infrastructure team mates to bring your model to production. Monitor your model, learn from the outcomes it drives. Understand how your model is impacting Upworks customers. Iterate accordingly.
• Ensure that the Trust & Safety model suite stays ahead of emerging threats by incorporating cutting-edge machine learning and AI into our detection suite. We are boundary pushers, using the latest technology to stay ahead of bad actors and constantly improve our detection capabilities.
• Drive operation excellence across the team by contributing to shared infrastructure, data, and feature pipelines, monitoring systems, and comprehensive documentation - anything that helps the team build better and faster.
• Share your knowledge and experience to improve team outcomes. Through pair programming, code review, journal club presentations, and feedback, your collaboration will amplify the impact of your teammates, ensuring your contributions extend far beyond your individual work.

Required Skills And Qualifications:

• ML Proficiency: Strong knowledge of core ML methods (model architectures, embeddings, NLP / language models). Expertise in Python and developing clean, reusable code that facilitates fast iteration. Familiarity with key ML libraries (e.g., scikit-learn, PyTorch, XGBoost/LightGBM, Transformers, etc.). Expert at model evaluation and assessing model impact.
• Model deployment and serving. Proven experience in deploying ML models to serving environments. Proficiency with deploying data/feature pipelines, and operating in cloud environments (AWS, GCP, Azure, etc).
• Project Management & Collaboration: Strong ability to scope, manage, and deliver projects on time. Excellent cross-functional collaborator. Leads with initiative, ability to navigate ambiguity. Proven experience in autonomously building machine learning solutions from ideation through deployment.
• Communication & Documentation: Excellent verbal and written communication skills. Proficient in writing design docs and communicating technical ideas clearly.

Nice-To-Have Skills:

• Advanced ML expertise: Experience in advanced areas of ML / AI (including, but not limited to, graph methods, anomaly detection, sequence modelling, multi-model / mixture of experts, vector retrieval (Faiss, etc), contrastive learning) 
• Generative AI, agentic workflows: Experience with generative AI and agentic workflows, including fine-tuning, RAG, agents, and serving.
• Trust & Safety/Risk Management: Previous experience working in trust and safety or risk management domains.

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