Senior Full Stack Engineer

You are a T-shaped full stack engineer with a spike in backend engineering. You will work with product and engineering on the product overall and support our infrastructure so we can scale efficiently. You do not need to be a DevOps wizard, but experience with AWS and Kubernetes is helpful.

You are adaptable, take initiative and enjoy contributing at pace. The team is lean, the environment is low-structure and clear communication matters. You will own our back-end infrastructure from AWS to our workflow automations in n8n and help the company grow from $3M to $30M ARR. We are still early in building our infrastructure, so you will play a key role in shaping what we build and how we build it.

What You'll Be Doing

• Owning and scaling Kernel's back-end infrastructure (AWS, NodeJS, Postgres, n8n)
• Designing systems that process millions of accounts, terabytes of data and thousands of parallel executions
• Building reliable, scalable data pipelines and services for enterprise customers
• Rapidly shipping features to users and to our internal team
• Continuously evaluating code, infrastructure and processes to find improvements
• Automating manual processes to increase speed and reliability
• Sharing feedback on roadmap direction and ideas on bets to place and potential solutions

More specifically you will be:

• Ensuring a world-class, scalable back-end infrastructure
• Working daily with the team to move us toward our next goal
• Implementing mass-action workflows that clean, enrich and complete CRM data
• Establishing robust monitoring, observability and reliability practices
• Partnering with product and solutions engineering to translate business logic into safe mass actions in customer CRMs

Do you have experience in TypeScript?, Do you have a Master's degree?, * 6+ years of software or product engineering experience

• Strong experience building and scaling production systems
• Comfort with backend engineering (NodeJS, Postgres, TypeScript)
• Strong understanding of LLM application development patterns (RAG, prompt engineering, consistency testing) - either through production experience or demonstrated personal projects. If you're new to LLMs but have shipped complex production systems in other domains and are comfortable rapidly learning new technologies, we'd still love to hear from you.
• Ability to operate autonomously in a small, high-velocity team
• Excellent communication and collaboration skills
• Some experience with Kubernetes or distributed systems

It is a plus if you also have:

• Experience working on AI-driven or data-heavy products
• Frontend experience (NextJS, React, TypeScript)
• Understanding of CRM data models or workflow automation

This role may not be for you if you:

• Need lots of structure
• Will only ship pixel-perfect results before releasing
• Need a fully fleshed-out spec to code
• Are heavily indexed on DevOps and have spent less time on software engineering

This role is definitely not for you if you:

• Prefer fully remote work (this role requires at least 3 days a week in the office)
• Do not enjoy the intensity of an early-stage startup
• Have side hustles or other projects
• Want to manage a team, * Back-end (AWS): NodeJS, TypeScript, Postgres
• Front-end: NextJS, TypeScript, Tailwind
• Workflow automation: n8n

• Salary: £110,000 - £170,000 + equity
• 24 days holiday per year + bank holidays
• 2 weeks work-from-anywhere
• Pension plan
• Top-spec equipment and central London office
• Free dinner at the office
• Team events and dinners
• Work directly with the founders to scale the systems that power enterprise AI

Kernel provides enterprise RevOps teams with AI-powered company data for their CRMs. We raised $14M in Series A funding from top VCs and operators at Plaid, OpenAI, and Slack to build an AI-native alternative to Dun & Bradstreet's entity and hierarchy data. RevOps teams at Navan, Gong, Mistral, and AlphaSense use Kernel to run their organizations. At Kernel, we process 7B+ tokens a day and run 1.8M+ agents daily, all on an architecture that's intentionally simple: one database, one queue. It's minimalism built for massive scale (For reference, Wikipedia has 2.3B tokens in the English wikipedia). Our queue processes a daily volume on par with the entire Visa network's transactions (~365M/day). All of which has been built by a small but mighty team of engineers.