Backend Engineer for AI (Golang)

As a Backend Engineer for AI, you will build and expand our feature management products and APIs using Golang, with a sharp focus on integrating AI capabilities. Your role includes collaborating with frontend engineers, monitoring server-side performance, writing various types of tests, participating in code reviews, and improving engineering standards and processes.

A successful candidate for the Backend Engineer for AI role should have demonstrable experience in server-side web development, particularly with languages such as Golang, Java, or Python. Familiarity with building RESTful APIs, knowledge of data structures, distributed systems, and a commitment to high-quality code practices are essential to fit into our team's dynamic.

In this role, you'll primarily work with Golang, Redis, and NATS along with AI technologies and models, such as those provided by OpenAI and Anthropic, to build scalable application features and robust APIs. Your understanding of networking technologies is also beneficial for ensuring seamless performance.

While participating in on-call duties is part of our you-build-it-you-run-it culture, opting for off-hours on-call work is entirely voluntary. Any on-call time you choose to take on is compensated, ensuring that you have the flexibility to balance work and personal life.

Our compensation range for the Backend Engineer for AI varies based on geography and level of experience, from $127,000 to $204,000. This base salary is accompanied by RSUs and comprehensive health, dental, vision, and mental health benefits.

In preparing for your interview, be ready to explain specific projects where you've utilized Golang. Highlight any challenges you faced, how you addressed them, and the impact of your contributions on the overall project success.

Show your knowledge by discussing techniques such as code optimization, proper use of caching mechanisms, and efficient database queries. Mention tools and methodologies you have used to monitor performance metrics.

Discuss your commitment to writing maintainable code, emphasizing practices like conducting thorough code reviews, implementing automated tests, and continuously integrating feedback from peers to improve code quality.

Share examples of your experience with LLMs, focusing on how you designed APIs or features to utilize AI effectively. Discuss any key learnings and how those experiences prepared you for this role.

Articulate your hands-on experience in creating, maintaining, and documenting RESTful APIs. Be prepared to cover tools you’ve used and how you've approached challenges in API performance and security.

Be prepared to delve into concepts like data consistency, fault tolerance, and how distributed systems operate. Illustrate your point with relevant experiences or projects that demonstrate your understanding.

Share your approaches to continuous learning, whether it's through online courses, attending meetups, following industry leaders on social media, or staying engaged with online communities of developers.

Use the STAR method (Situation, Task, Action, Result) to framework your response, highlighting a specific challenge relevant to backend development and your role in resolving it while improving the project's overall performance.

Reflect on the importance of open communication, teamwork, and collaborative problem solving in delivering high-quality products, citing specific instances where collaboration led to successful outcomes.

Discuss methodologies you use to ensure system reliability, such as regular monitoring, implementing alerts, proactive performance tuning, and being responsive in emergency situations. Highlight any relevant tools and practices that have worked for you.