• Enable product teams: teach, coach, and guide them on data and ML best practices • Lead by example: do complex data analysis and ML modeling, architecture, and implementation work as needed to accelerate teams while mentoring more junior data/ML folks. • Explore and analyze data using Python, pandas, and PySpark • Use matrix factorization, clustering, dimensionality reduction, and related techniques to understand and prepare data for modeling, and to identify and label latent factors (e.g., user behavior patterns, content/topic clusters, expertise dimensions). • Create, tune, and productionize ML models for categorization / classification, recommendations and similarity, and other prediction or ranking tasks that power product features. • Design and implement AI-driven ingest flows that turn unstructured inputs (tickets, emails, forms, messages, logs, etc.) into well-structured data that models and downstream systems can use. • Build workflows where AI can auto-fill or suggest key fields and metadata. • Work closely with engineers to integrate models and workflows into production systems with proper monitoring, fallbacks, and guardrails.

Role Description We are looking for a highly capable Senior MLOps Engineer with a strong Software Engineering and DevOps background. As a Senior MLOps Engineer, you will be embedded and supporting a revenue generation or cost optimization project, ensuring its success in production by: - Improving the code - Creating automated CI/CD testing - Developing frameworks that can be reused for other similar projects - Build, maintain, and document machine learning frameworks (python packages) used across multiple projects - Support a project team with Data Scientists, Business Stakeholders, Analysts, and Data Engineers - Develop reusable feature stores for rules-based and AI/ML models - Implement monitoring capabilities for model performance and effectiveness in production - Automate CI/CD testing and deployments incorporating MLOps best practices Qualifications - Bachelor's degree in software engineering, computer science, data science, mathematics, or a related field - 5+ years of overall experience in Data Analytics - 3+ years of experience with ML Engineering and/or ML Ops (Up to 2 years of Software Engineering or Data Engineering experience can also count towards this requirement) - Sharp critical thinking skills and ability to learn and question complex processes and solutions - Experience building scalable machine learning systems and data-driven products working with cross-functional teams - Experience creating python packages - Well-developed software engineering skills, including use of proper development, QA, and production environments, object-oriented programming, version control, and knowledge of multiple programming languages - Proficiency in Python and experience with common data analytics packages (e.g. Numpy, Pandas, Sklearn, PySpark) - Proficiency in SQL Company Description Capgemini is an AI-powered global business and technology transformation partner, delivering tangible business value. We imagine the future of organizations and make it real with AI, technology and people. With our strong heritage of nearly 60 years, we are a responsible and diverse group of 420,000 team members in more than 50 countries. We deliver end-to-end services and solutions with our deep industry expertise and strong partner ecosystem, leveraging our capabilities across strategy, technology, design, engineering and business operations. The Group reported 2024 global revenues of €22.1 billion.

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).