What you’ll bring to the team
- Strong programming skills, preferably in Python.
- Solid software engineering fundamentals, including testing, debugging, and system design.
- Experience building applications, projects, or products using LLMs and modern AI tools.
- Ability to design experiments, interpret results, and make data-driven decisions.
- Strong communication skills and willingness to collaborate across disciplines.
- Curiosity, ownership, and a desire to learn quickly.
What makes you stand out from others
- Experience building AI agents, copilots, or workflow automation systems.
- Experience designing evaluations, benchmarks, or testing frameworks for AI systems.
- Familiarity with OpenAI, Anthropic, Gemini, or open-source LLM ecosystems.
- Experience with retrieval systems, vector databases, and RAG architectures.
- Familiarity with LangGraph, OpenAI Agents SDK, MCP, or similar agent frameworks.
- Experience with observability, tracing, and production monitoring for AI systems.
- Exposure to cybersecurity, security operations, or developer tooling.
- Open-source contributions, research projects, or personal AI products.
Example projects
- Building evaluation suites that measure the quality of AI-driven security investigations.
- Creating automated regression tests to detect model and prompt regressions.
- Improving agent reasoning, planning, and tool-use capabilities.
- Developing datasets and benchmarks that reflect real customer workflows.
- Building systems that automatically identify and categorize agent failures.
- Designing feedback loops that continuously improve production agent performance.
Who thrives at Crogl
The strongest candidates for this role are usually builders.
You may have experience as a software engineer, ML engineer, researcher, AI engineer, or founder. What matters most is your ability to learn quickly, work independently, and ship impactful systems. We’re particularly excited about candidates who have:
- Built and deployed AI agents.
- Created evaluation frameworks for LLM applications.
- Published AI projects, demos, or open-source contributions.
- Developed tools that other people actively use.
- Strong opinions about what makes AI systems reliable and useful.
If you’ve spent nights and weekends building AI products because you genuinely enjoy it, you’ll probably fit in well here.
What we value
We care more about demonstrated ability than specific credentials.
You might be a great fit if you’ve
- Built AI products, agents, or developer tools that people actually use.
- Created evaluation frameworks, benchmarks, or testing systems for LLM applications.
- Contributed to open-source AI projects.
- Conducted independent research or published technical writing.
- Built ambitious side projects and iterated on them based on real-world feedback.
- Shown exceptional curiosity and a track record of learning quickly.
We’re especially interested in candidates who can show us what they’ve built. GitHub repositories, demos, blog posts, open-source contributions, benchmarks, evaluation frameworks, AI products, and side projects are all valuable signals.
We don’t expect candidates to check every box. If you’re excited about the role and believe you can contribute, we’d encourage you to apply.