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Build the future of financial services distribution.

We're a small team moving fast. Every hire shapes the company. If you want to do the most important work of your career, read on.

Founding AI EngineerEngineering
Full-time · San Francisco

You will help define how Kinro builds software in the age of agents. This is a hands-on role for someone who has built AI products end-to-end, trusts agentic coding as a force multiplier, and can ship across product, backend, evals, and infrastructure with strong systems judgment.

What you'll do
  • —Build AI product systems end-to-end, from tool use and backend services to evals and production monitoring.
  • —Use agentic coding tools aggressively and responsibly: break down large problems, set guardrails, and review generated work with high standards.
  • —Improve agent quality, latency, safety, and cost through better architectures, feedback loops, and evaluation harnesses.
  • —Work across the stack wherever leverage is highest, including product surfaces, infra primitives, and customer deployments.
  • —Turn one-off lessons from production into reusable product and platform capabilities.
What we're looking for
  • —You have built AI products, agent workflows, or LLM systems end-to-end in production.
  • —You know when to trust an agent, when to intervene, and how to structure work so the agent succeeds.
  • —You understand tool calling, latency, reliability, security, and cost tradeoffs in agentic systems.
  • —You can reason across the stack: application code, APIs, databases, cloud systems, and operational tooling.
  • —You have strong product taste and care whether something feels simple, fast, and reliable for the end user.
Nice to have
  • ·Experience with evals, model routing, prompt injection defenses, sandboxing, or other AI systems safety work.
  • ·Experience in high-ownership product environments where engineers talk directly to users and ship quickly.
  • ·Experience in fintech, insurance, or other regulated environments.
Apply for this role →or send us anything at [email protected]
Founding Software Engineer, InfrastructureEngineering
Full-time · San Francisco

You will build the infrastructure backbone behind Kinro's agents: cloud architecture, deployment systems, observability, data flows, and internal tooling. This is a coding-heavy role for someone who trusts agentic coding as a force multiplier and can help a small team ship quickly without compromising reliability, security, or simplicity.

What you'll do
  • —Design and own the infrastructure that runs Kinro agents in production, including cloud services, networking, data systems, and deployment pipelines.
  • —Use agentic coding tools aggressively and responsibly to ship infrastructure and internal tooling faster, with strong planning, guardrails, and review.
  • —Build observability, debugging, and reliability tooling so issues are easy to catch and fast to fix.
  • —Improve our security posture across application infrastructure, secrets management, sandboxing, and customer integrations.
  • —Create internal developer tooling and abstractions that let the team ship faster with less operational drag.
  • —Make strong architectural tradeoffs: choose systems that are simple, robust, and right-sized for our stage.
What we're looking for
  • —You have built and operated production infrastructure in a fast-moving startup or product environment.
  • —You are hands-on in code, not just configuration, and are comfortable working in TypeScript, Python, Go, or similar.
  • —You are excited by agentic coding and know how to break down infrastructure work so an agent can help effectively without creating operational risk.
  • —You have strong judgment around cloud architecture, CI/CD, databases, queues, security boundaries, and failure modes.
  • —You care about simplicity. You know when extra infrastructure is justified and when it is unnecessary complexity.
  • —You are comfortable partnering closely with product and customer-facing engineers to unblock real deployments.
Nice to have
  • ·Experience with agent platforms, async job systems, event-driven architectures, or LLM production infrastructure.
  • ·Experience with infrastructure-as-code and internal platform tooling.
  • ·Experience in regulated environments or enterprise customer deployments.
Apply for this role →or send us anything at [email protected]
Founding Forward Deployed EngineerEngineering
Full-time · San Francisco

You sit at the intersection of engineering and customer success. You will own deployments end-to-end — from the first scoping call to a live agent in production — and feed hard-won field knowledge back into the product. This role is rare: part engineer, part solutions architect, part trusted advisor to our clients.

What you'll do
  • —Own the technical deployment of Kinro agents at customer sites, from kickoff to go-live.
  • —Write integration code, scripts, and tooling to connect Kinro to client systems.
  • —Be the primary technical point of contact for customers post-sale.
  • —Identify patterns across deployments and bring them back as product requirements.
  • —Work with the founding team to develop repeatable deployment playbooks.
What we're looking for
  • —3+ years of experience in a forward deployed, solutions engineering, or implementation role.
  • —Comfortable reading and writing code (Python, TypeScript, or similar).
  • —Excellent communication — you can explain a technical constraint to a non-technical stakeholder.
  • —Customer-obsessed: you find satisfaction in making someone's workflow significantly better.
  • —High autonomy — you can manage a deployment end-to-end without hand-holding.
Nice to have
  • ·Experience deploying AI or SaaS products in financial services or insurance.
  • ·Familiarity with REST APIs, webhooks, and enterprise SSO/data pipelines.
Apply for this role →or send us anything at [email protected]
Founding ML EngineerEngineering
Full-time · San Francisco

You will help close the gap between promising model behavior and dependable product behavior. This is a production-oriented ML role for someone who can take models, evals, and agent improvements out of notebooks and into systems that are fast, reliable, measurable, and safe to operate.

What you'll do
  • —Improve the performance of our agents in production through better prompts, model selection, evals, retrieval, fine-tuning, and system design.
  • —Build and own the ML workflows that let us test, measure, and ship model changes safely.
  • —Develop offline and online evaluation systems tied to real business metrics like conversion, compliance, latency, and customer experience.
  • —Work closely with product and engineering to turn ambiguous failures in production into concrete model and system improvements.
  • —Help build the infrastructure, data pipelines, and tooling needed to make ML development fast, repeatable, and production-grade.
What we're looking for
  • —You have shipped ML or LLM systems into production and have seen what breaks outside the demo environment.
  • —You are strong in Python and comfortable owning the surrounding engineering work, not just the modeling layer.
  • —You have good judgment about tradeoffs across model quality, latency, cost, observability, and operational complexity.
  • —You are excited to work in a highly iterative environment where the loop is ship, measure, debug, and improve.
  • —You care about outcomes. You want to improve the real system, not just optimize benchmark numbers in isolation.
Nice to have
  • ·Experience with LLM evals, model routing, fine-tuning, RAG, or tool-using agents.
  • ·Experience building internal ML platforms, data pipelines, or experimentation infrastructure.
  • ·Experience in fintech, insurance, or other regulated environments where correctness and auditability matter.
Apply for this role →or send us anything at [email protected]
Founding Research ScientistResearch
Full-time · San Francisco

You will define how our agents think, reason, and improve. This is not a role for researchers who want to stay in the lab — you will own the full loop from hypothesis to eval to production. You'll work on hard problems: grounded generation, compliance-aware reasoning, multi-turn sales conversations, and agent-to-agent negotiation.

What you'll do
  • —Design and run evaluations that measure what actually matters: conversion, compliance, satisfaction.
  • —Research and implement improvements to our core agent capabilities.
  • —Own the eval infrastructure and ensure every model update is measurable and safe to ship.
  • —Stay current with the frontier of LLM research and rapidly integrate relevant advances.
  • —Collaborate with engineering to take research from notebook to production.
What we're looking for
  • —PhD or equivalent research experience in ML, NLP, or a related field.
  • —Track record of shipping research into real systems — papers are great, deployed models are better.
  • —Deep familiarity with LLMs, fine-tuning, RLHF/RLAIF, and evaluation methodology.
  • —Strong Python skills; comfortable with PyTorch or JAX.
  • —Clear thinking and clear writing — you can explain your ideas to engineers and customers alike.
Nice to have
  • ·Experience with agentic systems, multi-agent frameworks, or tool use.
  • ·Knowledge of regulatory constraints in financial services (FCA, GDPR, etc.).
  • ·Prior work at a research lab (DeepMind, Meta AI, OpenAI, etc.).
Apply for this role →or send us anything at [email protected]

Don't see your role? We're always interested in exceptional people. Send a note to [email protected].

Meet the team →