Description
Position at WebMD
Medscape, a division of WebMD, develops and hosts physician portals and related mobile applications that make it easier for physicians and healthcare professionals to access clinical reference sources, stay abreast of the latest clinical information, learn about new treatment options, earn continuing medical education credits and communicate with peers.
WebMD is an Equal Opportunity / Affirmative Action employer and does not discriminate on the basis of race, ancestry, color, religion, sex, gender, age, marital status, sexual orientation, gender identity, national origin, medical condition, disability, veterans status, or any other basis protected by law.
Responsibilities :
- Design and build internal tools to support campaign planning, performance optimization, and automation
- Collaborate closely with Product Leads to translate strategic goals into technical solutions that increase internal efficiency and unlock new business value
- Own end-to-end development of AI-powered features, such as natural language interfaces, tactic recommendation engines, and data visualization tools
- Apply machine learning, algorithmic logic, or statistical modeling where appropriate to enhance precision of recommendations and insights
- Optimize back-end systems for speed, modularity, and scale
- Participate in architectural planning for scalable internal platforms that can support a growing suite of Applied Technology tools
- Conduct rapid prototyping and iteration based on feedback from Sales, Strategy, and Measurement teams
Qualifications : Education / Certifications :
BS / MS in Computer Science or Machine LearningDesired Experience :
1+ years' of professional or internship experienceProven experience developing Agentic AI systems using multi-agent frameworks like LangGraph or CrewAIDeep understanding of LLM orchestration, including :Memory (summarization, retrieval)Planning frameworks (ReAct, Tree of Thoughts, MRKL)Tool / function calling with OpenAI, Anthropic, or Gemini APIsReflection and self-evaluation loopsHands-on experience with Retrieval-Augmented Generation (RAG) using FAISS, Weaviate, or ChromaBuilt and deployed production-grade ML systems, including :Microservices for inference and preprocessingVector stores + retrievers for internal search and analytics agentsFeature stores and ML pipeline orchestration (Feast, Vertex AI)Proficient in end-to-end ML productization, CI / CD, Docker, Kubernetes, and deployment on AWS or GCPStrong SQL skills (joins, CTEs, window functions) with experience in BigQuerySolid frontend development background with React / Next.js and Tailwind CSSDesigned AI-driven user interfaces, including copilots, interactive dashboards, and dynamic filteringFamiliarity with JavaScript / TypeScript component libraries (e.g., shadcn / ui, Chakra UI) and data viz tools (D3.js, Recharts)Strong foundation in mathematics for machine learning, including linear algebra, calculus, probability, and optimization techniques used in model training and evaluationFamiliarity with discrete mathematics and graph structures, supporting logic-driven models and multi-agent orchestration frameworksFormal academic coursework in machine learning, statistical learning, or deep learning, with strong understanding of supervised / unsupervised learning and model evaluationNice to have :
Experience analyzing healthcare dataPublication or co-authorship in top-tier AI research venues (e.g., NeurIPS, ICML, CVPR, ACL), demonstrating thought leadership and contribution to cutting-edge ML innovationCompensation : $60,000 - $70,000
Benefits :
Employees in this position are eligible to participate in the company sponsored benefit programs, including the following within the first 12 months of employment :Health Insurance (medical, dental, and vision coverage)Paid Time Off (including vacation, sick leave, and flexible holiday days)401(k) Retirement Plan with employer matchingLife and Disability InsuranceEmployee Assistance Program (EAP)Commuter and / or Transit Benefits (if applicable)Eligibility for specific benefits may vary based on job classification, schedule (e.g., full-time vs. part-time), work location and length of employment.