Asks for

The ad says“Hands-on experience building with LLMs, agentic architectures, and AI workflows—such as LangGraph, Claude Agent SDK, OpenAI Agents SDK, or equivalent.”
The ad says“Hands-on experience building with LLMs, agentic architectures, and AI workflows—such as LangGraph, Claude Agent SDK, OpenAI Agents SDK, or equivalent.”
The ad says“A solid grasp of the mechanics of data science and applied AI: evaluation design, experimental rigor, noisy metrics, statistical reasoning, feedback loops, and evidence-based decision-making.”
The ad says“Operate what you build in production across Kubernetes, Argo Workflows, AWS, and GitOps.”
The ad says“Operate what you build in production across Kubernetes, Argo Workflows, AWS, and GitOps.”
The ad says“Hands-on experience building with LLMs, agentic architectures, and AI workflows—such as LangGraph, Claude Agent SDK, OpenAI Agents SDK, or equivalent.”
The ad says“Strong experience using coding agents such as **Claude Code, Codex, or Cursor** as part of your day-to-day engineering workflow.”
The ad says“Strong experience using coding agents such as **Claude Code, Codex, or Cursor** as part of your day-to-day engineering workflow.”
The ad says“Strong experience using coding agents such as **Claude Code, Codex, or Cursor** as part of your day-to-day engineering workflow.”
The ad says“Strong experience using coding agents such as **Claude Code, Codex, or Cursor** as part of your day-to-day engineering workflow.”
The ad says“A solid grasp of the mechanics of data science and applied AI: evaluation design, experimental rigor, noisy metrics, statistical reasoning, feedback loops, and evidence-based decision-making.”
The ad says“A solid grasp of the mechanics of data science and applied AI: evaluation design, experimental rigor, noisy metrics, statistical reasoning, feedback loops, and evidence-based decision-making.”
The ad says“Operate what you build in production across Kubernetes, Argo Workflows, AWS, and GitOps.”
The ad says“Operate what you build in production across Kubernetes, Argo Workflows, AWS, and GitOps.”
The ad says“Hands-on experience building with LLMs, agentic architectures, and AI workflows—such as LangGraph, Claude Agent SDK, OpenAI Agents SDK, or equivalent.”
The ad says“Hands-on experience building with LLMs, agentic architectures, and AI workflows—such as LangGraph, Claude Agent SDK, OpenAI Agents SDK, or equivalent.”
The ad says“Our stack: Python, LangGraph, Claude Agent SDK, Langfuse, Kubernetes, Argo Workflows, AWS, GitOps, and MCP.”
The ad says“Hands-on experience building with LLMs, agentic architectures, and AI workflows—such as LangGraph, Claude Agent SDK, OpenAI Agents SDK, or equivalent.”
The ad says“Our stack: Python, LangGraph, Claude Agent SDK, Langfuse, Kubernetes, Argo Workflows, AWS, GitOps, and MCP.”
The ad says“Strong software engineering skills and an experiment-driven mindset: form a hypothesis, build, measure, learn, and iterate.”
The ad says“A solid grasp of the mechanics of data science and applied AI: evaluation design, experimental rigor, noisy metrics, statistical reasoning, feedback loops, and evidence-based decision-making.”

🌍 We’re making security suck less for developers.
Security tools haven't kept pace with modern software development. They interrupt teams, slow releases, and turn security into a bottleneck. Aikido is changing that. We build developer-first security products that reduce real risk without getting in the way of shipping software. We focus on what matters, automate the rest, and make security a capability, not a blocker.
Founded in 2022 by third-time founders, Aikido has $85M in the bank and the runway to build for the long term.

Following our acquisition of Root.io in June 2026, the team formed Agentic Factory: the business unit moving beyond vulnerability detection to automated remediation.

We are looking for an AI Engineer to build the systems that drive this: AI agents that investigate vulnerabilities, generate and validate code fixes, and continuously refine their performance. In this role, you’ll work at the intersection of AI engineering, security automation, and rigorous evaluation to translate cutting-edge models into dependable production tools.

Responsibilities

Build and evolve production AI agent pipelines for CVE research, patch generation, validation, container image remediation, and end-to-end merge requests.
Design the agent capabilities behind those workflows: prompts, tools, model selection, context, guardrails, and integrations.
Build the feedback loops that make our systems better: tracing production runs, creating datasets, defining evals and scorers, and using results to guide iteration.
Analyze real-world performance to answer the questions that matter: Did this change improve patch success, quality, latency, or cost? Can we prove it?
Operate what you build in production across Kubernetes, Argo Workflows, AWS, and GitOps.
Partner closely with Product and engineering to focus on the customer problems with the highest security impact—and ship quickly.

Hands-on experience building with LLMs, agentic architectures, and AI workflows—such as LangGraph, Claude Agent SDK, OpenAI Agents SDK, or equivalent.
Strong experience using coding agents such as **Claude Code, Codex, or Cursor** as part of your day-to-day engineering workflow.
A solid grasp of the mechanics of data science and applied AI: evaluation design, experimental rigor, noisy metrics, statistical reasoning, feedback loops, and evidence-based decision-making.
The judgment to distinguish an impressive demo from a system that reliably works in production.
Strong software engineering skills and an experiment-driven mindset: form a hypothesis, build, measure, learn, and iterate.
Comfort working independently in a fast-moving environment with high ownership and little unnecessary process.
Clear communication and good product instincts.

Nice to have

Experience in cybersecurity: CVEs, containers, software supply chain security, or pentesting.
Experience working with open-weight models, including model selection, serving, inference optimization, or fine-tuning.
Familiarity with Langfuse or similar observability and evaluation platforms.
Experience with Go or TypeScript.

What you can expect

Short decision lines and meaningful ownership from day one.
The freedom to shape important projects and ship quickly.
Top-tier compensation and equity.
High trust, strong values, and no corporate theatre.
Flexible working hours and an open, informal culture.
Our stack: Python, LangGraph, Claude Agent SDK, Langfuse, Kubernetes, Argo Workflows, AWS, GitOps, and MCP.

Asks for

Values