Software Engineer

This is not a normal software engineering role At Clear Feed, using AI to build software is not optional. Every engineer is expected to use AI every day — to understand code, write code, review code, debug issues, design product flows, generate tests, write docs, explore APIs, and ship faster. We expect you to be comfortable using tools like Claude, Cursor, Chat GPT, Codex, or similar AI tools as part of your default engineering workflow. If you do not know how to use AI effectively to write code, debug issues, understand unfamiliar systems, and improve your own engineering output, please do not apply. We are looking for engineers who can use AI as leverage — while still bringing strong judgment, ownership, and engineering taste. AI can write a lot of the first draft. You are responsible for the final result.

What we are building

Clear Feed is building an AI-first support platform for modern teams working in Slack and Microsoft Teams. Our product connects conversations, tickets, knowledge bases, engineering tools, CRMs, and AI agents so companies can resolve customer and internal requests faster. Our engineers work on real product problems:

• How should an AI agent understand a support conversation?
• When should it answer automatically, and when should it ask a human?
• How do we safely connect Slack, Teams, Zendesk, Jira, Git Hub, Linear, Salesforce, Hub Spot, and other tools?
• How do we make AI workflows observable, debuggable, and trustworthy?
• How do we ship fast without creating chaos for customers?

What you will do

You will take ambiguous customer problems and turn them into working software. That may include API design, backend logic, database changes, Slack/Teams workflows, AI-agent behavior, product UX, tests, monitoring, and deployment. You will be expected to:

• Use AI tools heavily while writing and reviewing code.
• Use AI to create first-pass designs, specs, implementation plans, tests, and docs.
• Build backend services using Type Script, Node.js, Nest JS, Postgre SQL, Redis, queues, and cloud infrastructure.
• Work on AI-agent features involving LLMs, RAG, tool-calling, structured outputs, evals, guardrails, and observability.
• Own features end to end: idea → design → implementation → testing → release → production debugging.
• Debug production issues and improve the system after every incident.
• Improve our engineering system itself: better prompts, better internal tools, better test generation, better review loops, better deployment workflows.
• Move fast, but stay accountable for what ships.

What “AI-native” means here We do not mean “vibe code and hope it works.” We mean:

• You can use AI to move significantly faster.
• You can use AI to write, refactor, test, and debug code.
• You can review AI-generated code critically.
• You understand that AI is often confidently wrong.
• You can use AI for product thinking, but you do not outsource judgment.
• You can ship faster without lowering quality.
• You can explain your decisions clearly to humans.

The best engineers in the next decade will not be the ones who manually type the most code. They will be the ones who combine product judgment, technical depth, and AI leverage to ship high-quality software faster than traditional teams.

What we are looking for

You may be a good fit if:

• You are already using AI coding tools daily.
• You are faster today than you were one year ago because of AI.
• You have strong software fundamentals and do not blindly trust generated code.
• You can work across backend, product, integrations, and basic frontend when needed.
• You care about customer experience, not just clean abstractions.
• You enjoy working in small teams with high ownership.
• You can take a problem statement and come back with a working solution.
• You are comfortable with ambiguity.
• You like shipping.
• You are curious, direct, and low-ego.

Bonus points We will pay extra attention if you have:

• Built something real using AI agents.
• Used Claude Code, Cursor, Codex, v0, Lovable, Replit, or similar tools seriously.
• Shipped a side project using AI.
• Built integrations with Slack, Teams, Zendesk, Jira, Git Hub, Salesforce, or Hub Spot.
• Worked on support, developer tools, workflow automation, or B2B Saa S products.
• Written about your engineering work.
• Open-source contributions or visible proof of work.

How we evaluate We use AI in our hiring process for the same reason we use it in engineering: it helps everyone move faster and spend time where human judgment matters most. The first step is a short AI-led interview of around 15 minutes. It helps us understand your background, your experience, and how you think about building software. After that, you will have three technical discussions with our team:

• Problem solving + debugging
• Problem solving
• System design

We want to understand how you reason, how you debug, how you make trade-offs, and how well you can combine engineering fundamentals with AI tools to build reliable software faster.

What you get

We want engineers to have the tools, support, and environment to do their best work. Flexible AI Credits You will get flexible AI credits and access to the tools you need to build faster — Claude, Cursor, Chat GPT, Codex, or whatever genuinely helps you ship better software. We do not want engineers hesitating before using AI because of subscription limits or tool access. Health and medical support You get comprehensive medical insurance, along with free doctor consultations. We are a small team, but we want to be thoughtful about health, family, and emergencies. You should not have to figure everything out on your own when something medical comes up. Sports and team activities We sponsor badminton and cricket games for the team. Not mandatory forced-fun activities. Just real sports, real games, and a good way to spend time with people outside tickets, PRs, and production issues.