Founding Engineer, Backend

Our Vision

Clarity is an operating system for revenue in a business. An AI-powered workspace that sales, customer support, and product work out of to close customers, prevent churn, and build product.

We are passionate about building high-quality software and products that people love. We have growth mindsets and are excited about pushing ourselves to be the best we can be.

We think we’re onto something big, and are doing everything we can to make it happen.

Your Mission

The vision for Clarity is powered by a massive amount of data that needs to be ingested from tools across the stack, reliably processed and analyzed, and then served to the user instantly. Our customers rely on Clarity to build their businesses and need it to work reliably and correctly every single day. We are hiring a founding engineer to lead backend and infrastructure development.

We’re a small team that moves quickly while keeping a high quality bar. We’re looking for someone who is excited about taking ownership of large parts of Clarity’s product and AI stack.

We’re looking for someone who is passionate, optimistic, and has a deep-rooted desire to build something great (and to have fun while doing it).

Your Responsibilities

Backend Engineering

1. Architect and own our data processing pipeline end-to-end
2. Create maintainable abstractions to enable the engineering team to build reliably
3. Define the data ontology across countless integrations that Clarity supports
4. Set the culture for quality and efficacy for the engineering team

Infrastructure

1. Own the deployment process, ensuring consistent and stable releases
2. Scale Clarity’s infrastructure as we scale to ensure uptime
3. Own observability and monitoring for the stack
4. Set team processes around on-call and incident responses

Full-stack Engineering

1. Build full-stack experiences in the Clarity product end-to-end
2. Proactively addressing bugs and fixing paper cuts
3. Jump in to support customers when they run into issues or have requests

Generative AI Infrastructure

1. Ensure we can trace issues and debug effectively with strong monitoring and observability
2. Establish an evaluation framework to ensure response quality as we make changes
3. Optimize foundation model costs without compromising on response quality
4. Build frameworks to make our AI systems modular and extensible