Top Tech Jobs & Startup Jobs

Onboard university and institutional clients to Elsevier’s Digital Commons platform, configure repositories, support editors and librarians, provide publishing guidance, train non-technical staff, and troubleshoot HTML/CSS issues. The role requires independent remote work, fluent Mandarin and English communication, strong written English, technical curiosity, and sound judgment when resolving or escalating client issues.

Design, configure, maintain, and troubleshoot cloud infrastructure across multiple environments. Develop automation and Infrastructure as Code, support monitoring and incident response, participate in change management, and apply cloud, DevOps, security, and reliability best practices. Collaborate with development, support, and platform teams to improve enterprise systems and operational excellence.

Owns product marketing strategy and market execution for Mytonomy, Patient Education, and Care Planning across the North American healthcare provider nursing market. Leads integrated campaigns, product launches, thought leadership, content, events, demand generation, sales enablement, competitive positioning, budget management, and performance measurement. Partners cross-functionally to influence priorities, connect portfolio offerings, generate commercial outcomes, and provide market and customer insights to leadership.

As a Senior ML Ops Engineer, you will bridge Data Science and Engineering to automate ML workflows, manage CI/CD for ML systems, and collaborate with various teams to implement solutions for AI features and services.

Owns the digital product data lifecycle for Elsevier’s Enterprise Product Hub, including requirements, prioritization, roadmap development, governance, system onboarding, and publication across business areas. The role makes product and scope decisions under ambiguity, partners with business and engineering stakeholders, supports adoption and documentation, and coaches junior and mid-level analysts while improving data quality and customer buying experiences.

Leads AI and data science strategy across machine learning, NLP, search, recommendation, experimentation, and generative AI. Manages technical teams, develops talent, and oversees AI systems from experimentation through production. Builds LLM, RAG, semantic search, and knowledge-discovery solutions; establishes evaluation frameworks, quality metrics, experimentation practices, and responsible AI standards. Partners with product, technology, and business leaders to shape roadmaps and deliver measurable customer and business outcomes.

Designs, develops, tests, and maintains scalable identity and access management applications using Java and JavaScript. Responsibilities include implementing complex system components, resolving bugs, conducting code reviews, supporting CI/CD, and defining logging, instrumentation, and alerting requirements. The role collaborates with technical stakeholders, translates requirements into software designs, participates in Agile or Waterfall environments, performs pair programming, and mentors junior developers. Experience with identity providers, OIDC, databases, cloud infrastructure, containers, and Kubernetes is valuable.

Develop and maintain scalable identity and access management applications using Java and JavaScript. Design moderately complex software components, write quality code, perform unit testing, review specifications, resolve defects, and collaborate in Agile teams. Work with identity providers, authentication flows, OIDC clients, CI/CD pipelines, GitHub workflows, AWS container platforms, and complex data models. Familiarity with AI-assisted development, Terraform, Docker, and Kubernetes is beneficial.

Owns the full sales cycle for complex, high-value AI research workspace opportunities, from prospecting and discovery through negotiation and closure. Builds executive stakeholder relationships, generates new business, supports strategic renewals, leads workshops, develops business cases, and communicates measurable value from AI-powered research and knowledge workflow solutions. Requires enterprise sales expertise, AI fluency, consultative selling skills, and extensive regional travel.

Design, develop, and deploy ML, NLP, and generative-AI solutions (including LLMs and RAG) to improve search, retrieval, recommendation, and question-answering across scientific publications and datasets. Build scalable pipelines, evaluation frameworks, and production-quality Python code while collaborating with engineering, product, UX, and domain experts to measure and improve model reliability, relevance, and user impact.