Compensation: $125k+ base & meaningful founder-level equity
Overview
We launched a few weeks ago and early feedback/demand has been strong. We’re backed by founders from Apollo, HubSpot, Typeform, and more and are looking for one killer engineer to help us build something people truly want & need. We need a hands-on, full-stack builder who can ship end to end, own deployments, and stand up data and agent infrastructure that scales from the first customers to the next hundred.
What you’ll do
- Build end to end: Design, ship, and own features across the stack (frontend, backend, data, agents).
- Own infra & deployment: Stand up CI/CD, environments, and runtime reliability. You will be the person who gets code to prod and keeps it healthy.
- Monorepo stewardship: Set conventions, tooling, and module boundaries for a fast, clean monorepo.
- Data pipelines: Design and run ETL/ELT flows that are observable, testable, and cost-aware.
- Agentic systems: Build and ship agent workflows, tools, and evaluators with clear success metrics.
- Quality & speed: Write design docs, add tests and telemetry, and move quickly without breaking trust.
- Founder collaboration: Work directly with founders on product direction and customer feedback loops.
You might be a great fit if you have
- Full-stack chops: Strong TypeScript and Python. You’ve shipped serious Next.js frontends and service backends (REST or GraphQL).
- Data engineering: Practical ETL/ELT design, orchestration, schema versioning, and monitoring. Familiar with tools like Airbyte/Fivetran, dbt, Dagster/Airflow (or equivalent approaches).
- DevOps experience: CI/CD (GitHub Actions or similar), containers, infrastructure as code, and either Kubernetes, ECS, or a PaaS. Comfortable owning prod.
- Monorepo experience: Turborepo or Nx, shared packages, caching, build optimization.
- Agentic workloads: You’ve built and deployed LLM agents or tool-using workflows. You understand prompts, tools, memory, and failure modes.
- Agent evals: You’ve set up evaluation harnesses and regression suites for agents or LLM features (e.g., offline eval sets, trace scoring, task success metrics).
- Startup context: 0→1 or early-stage experience at Seed to Series B companies. You are comfortable with ambiguity, high ownership, and direct customer exposure.
Other great experience
- Mastra experience (or similar agent frameworks) and having “fielded” agents in production.
- Porter (Porter.run) or comparable PaaS experience for app delivery.
- Observability for LLMs: Langfuse, LangSmith, OpenTelemetry, or custom tracing.
- RAG and data systems: Vector stores, embeddings, chunking strategies, and retrieval evals.
- Security & auth: SSO/OIDC, secrets management, role-based access, audit trails.
Our stack (indicative)
- Frontend: Next.js, React, TypeScript
- Backend: Node/TypeScript and Python services, REST/GraphQL
- Data: Postgres, Redis, warehouse/lake, ETL/ELT with orchestration
- Infra: Docker, CI/CD with GitHub Actions, IaC, Kubernetes/ECS or PaaS (e.g., Porter/Vercel)
- AI/Agents: Mastra or equivalent, OpenAI/Anthropic, evaluation harnesses, tracing/observability
How we work
- Bias to action, tight feedback loops with users, written design docs, and pragmatic testing.
- Clear ownership, transparent metrics, and direct access to founders.
We like to work hard, have fun, and take care of each other.
Note: This is a refined job description for clarity and formatting; the original notes about general postings have been removed to focus on the role.