Weave

Weave

Principal Engineer - GenAI Applications & MLOps

US Remote · Principal · Contract

Sponsorship not specifiedDetected 67 days ago
Distributed SystemsNoSQLPostgreSQLRedisAWSGCPCloud PlatformsKubernetesMachine LearningLLMsRAGAgentic AIMLOpsRecruitingLeadershipCommunicationProblem SolvingMentoringWriting

About the role

  • This is a strategic leadership position requiring deep expertise in MLOps and GenAI.
  • You will consult with teams on common ML patterns and tradeoffs, ensuring that our technical strategy for data and intelligence positions Weave as a leader in the healthcare communication space.
  • AI may assist with things like writing job descriptions, scheduling interviews, or reviewing applications against job-related criteria.

Responsibilities

  • Design and develop ML infrastructure, tooling, and models to help teams deliver world-class experiences
  • Build internal and external products and platforms to enable teams to incorporate AI into their features and customer-facing products
  • Translate product goals into actionable engineering plans and build scalable, resilient services for data integration and event processing

Requirements

  • 12+ years of software engineering experience with progressive technical leadership scope
  • Expertise with modern ML tools and techniques such as LLMs, RAG, Prompt Engineering, Fine Tuning, LLM evaluations, multi-modal models, and others
  • Operational experience with cloud-native infrastructure on GCP or AWS, including Kubernetes, infrastructure-as-code, and highly available system design
  • Track record of leading cross-team technical initiatives that delivered measurable business outcomes
  • Expertise with customer facing GenAI at scale, in production

Visa & Work Authorization

  • roblem-solve and progress regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, or other applicable legall
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This listing is sourced directly from Weave's careers page and normalized into a canonical job model.