What You’ll Accomplish
- Build and enhance high-performance, user-friendly frontend applications that power our customer-facing AI products
- Collaborate with Product and Design to create seamless experiences while maintaining code quality and avoiding tech debt
- Take ownership of frontend scalability, reliability, and performance, ensuring fast and accessible applications
- Proactively prevent regressions through robust testing and monitoring strategies
- Continuously improve architecture by advocating for best practices in reusability, state management, and maintainability
Your Expertise
- 7+ years of professional experience in frontend engineering with experience in modern frameworks like React
- Hands-on experience with GraphQL and schema design. Bonus: experience with building CMS tech, accessibility, Node.js, REST APIs, etc.
- A knack for understanding UX, and translating Figma designs into high-quality interfaces
- A proven track record of building applications that maintain a high bar of quality, and experience monitoring client-facing applications with tools like Datadog RUM
- Solid understanding of software development best practices, including code reviews, writing tests, and continuous integration
- While you’re generally focused on the frontend, you are still curious and eager to learn about the backend, databases, distributed systems, network communication, and other full-stack technologies
- Proven ability to collaborate effectively with cross-functional teams
Nice to Haves
- Experience in backend software engineering focusing on backend systems
- Proficiency in Java, Python, or Go, with a strong understanding of object-oriented programming
- Experience building and maintaining scalable, high-performance applications
- Experience with services-oriented architecture and distributed systems
- Familiarity with AWS services and cloud infrastructure
- Knowledge of databases such as DynamoDB, Postgres, or Redis
- Experience with messaging systems or streaming platforms (e.g., Kafka, Pulsar)
- Experience with DevOps practices and tools such as Docker and Kubernetes
What We Use
- Our infrastructure runs primarily in Kubernetes hosted in AWS’s EKS. Infrastructure tooling includes Istio, Datadog, Terraform, CloudFlare, and Helm
- Our backend is Java / Spring Boot microservices, built with Gradle, coupled with things like DynamoDB, Kinesis, AirFlow, Postgres, Planetscale, and Redis, hosted via AWS
- Our frontend is built with React and TypeScript, and uses best practices like GraphQL, Storybook, Radix UI, Vite, esbuild, and Playwright
- Our machine learning is driven by custom and open source machine learning models, lots of data and built with Python, Anyscale, Tecton, Metaflow, HuggingFace 🤗, PyTorch, TensorFlow, and Pandas
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