About The Position

We seek experienced Full Stack Software Engineers to build the applications and infrastructure that bring our diffusion LLMs to users. You will design and develop both frontend interfaces and backend services — creating intuitive experiences and robust APIs capable of serving at scale. You'll work closely with ML engineers and researchers to bridge the gap between complex AI models and user-friendly products.

Requirements

  • BS/MS/PhD in Computer Science, Machine Learning, or a related field (or equivalent experience).
  • 5+ years of experience building production web applications.
  • Strong proficiency in modern JavaScript/TypeScript and Python.
  • Experience with frontend frameworks (e.g., React) and state management solutions.
  • Solid understanding of backend development, including API design, database management, and microservices architecture.
  • Experience with SQL and NoSQL databases (e.g., Neon).
  • Familiarity with Kubernetes, CI/CD pipelines, and cloud infra (AWS and/or Azure).
  • Experience with version control (Git) and collaborative development workflows.
  • Strong problem-solving skills and the ability to work in a fast-paced startup environment.

Nice To Haves

  • Experience building applications that integrate with AI/ML systems.
  • Knowledge of streaming architectures and real-time data processing.
  • Experience with monitoring and observability tools (Prometheus, Grafana).
  • Understanding of ML concepts and experience with ML frameworks (PyTorch, TensorFlow).
  • Experience with infrastructure as code tools (Terraform).
  • Experience with testing frameworks and test-driven development.
  • Experience with UI/UX design and Framer.

Responsibilities

  • Design and develop scalable web applications and APIs for our models, building both frontend interfaces and backend services.
  • Build systems for internal experimentation and monitoring that provide observability across our entire tech stack.
  • Develop RESTful APIs to expose model capabilities to external applications.
  • Implement authentication, authorization, and security best practices for enterprise deployments.
  • Collaborate with ML engineers to integrate model serving infrastructure with the application layer.
  • Design infrastructure as code, deployment automation, and CI/CD pipelines.
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