Senior Software Engineer, Applied AI

Weights & Biases Weights & Biases · Data AI · Bellevue, WA +2 · Information Technology

Senior Software Engineer to design and build production-grade, full-stack AI-native analytics platforms and first-party applications that embed governed data directly into operational workflows. This role involves developing AI-enabled user experiences, scalable backend services, and intuitive interfaces, integrating AI/LLM capabilities into real-world applications, and working across the stack from React frontends to backend services on Kubernetes.

What you'd actually do

  1. design and build production-grade, full-stack applications that make data accessible, actionable, and embedded within CoreWeave’s core workflows.
  2. develop AI-enabled user experiences, scalable backend services, and intuitive interfaces that abstract away the complexity of underlying data systems.
  3. work across the stack - from React-based frontends to backend services running on Kubernetes - while integrating AI/LLM capabilities into real-world applications.
  4. take ownership of complex systems end-to-end, from design through deployment and iteration.

Skills

Required

  • 7+ years of experience building production-grade software applications
  • backend programming languages (Python, Go, Java, C#)
  • frontend programming languages (JavaScript, TypeScript)
  • building modern frontend applications using frameworks such as React and Next.js
  • containerization and deployment tooling (e.g., Docker, Helm)
  • building and operating APIs and services (e.g., REST, gRPC)
  • developing and deploying AI/ML/LLM-powered applications in production environments
  • data platforms and tools (e.g., Spark, Kafka, or similar systems)
  • implementing software development best practices, including CI/CD, automated testing, observability, and secure application design
  • take ownership of complex systems end-to-end

Nice to have

  • building AI-native applications such as text-to-SQL interfaces, copilots, or automated insight-generation systems
  • designing and building scalable, distributed systems deployed in cloud-native environments (e.g., Kubernetes)
  • real-time data processing or streaming architectures
  • building internal platforms or tools that enable self-service analytics or decision-making

What the JD emphasized

  • production-grade
  • production environments
  • production-grade software applications
  • real-world applications
  • scalable
  • scalable, distributed systems
  • scalable backend services
  • scalable and usable

Other signals

  • AI-native analytics platforms
  • AI-enabled user experiences
  • integrating AI/LLM capabilities into real-world applications
  • building and operating APIs and services
  • production-grade, full-stack applications