Founding AI Engineer

At Furl , we’re building the first agentic remediation platform for IT security — technology that closes the gap between identifying risk and actually fixing it. Today, most organizations surface more vulnerabilities than they can handle. We’re changing that by enabling safe, automated remediation across macOS, Windows, and soon Linux. We ingest security and operational signals, classify them into concrete remediation targets, and use an endpoint agent to collect real-time telemetry and execute fixes. Agentic workflows generate and validate fix plans, which are deployed safely at scale through policy-driven autonomy. This is real infrastructure work with real-world impact. We’re building systems that help teams focus on what truly matters — and we’re doing it with care, clarity, and craftsmanship. We’re a real startup with a real in‑person vibe. We meet 2‑3 days per week in Los Angeles to collaborate, build trust, and move fast together. It’s a fun environment, but we won’t sugarcoat it — the pace is demanding and sometimes uncomfortable through GA. We value ownership over hierarchy and believe great work comes from people who are trusted to think, experiment, and take responsibility. What You’ll Do As a Founding AI Engineer, you’ll design and build the AI systems that power autonomous remediation, from data collection and model training to orchestration services. Develop and train models for data classification, remediation generation, and action planning. Build pipelines for dataset creation, labeling, and evaluation. Integrate AI services into backend systems and user workflows. Collaborate with fullstack and agent engineers to connect AI output to customer environments. Your Experience Strong Python background; experience with frameworks like PyTorch, LangChain, or Hugging Face. Familiarity with data engineering, retrieval, or model fine‑tuning. Experience integrating AI inference into production systems (API orchestration, monitoring, etc.). Experience with leading large language models (LLMs), fine tuning, and performance. Previously shipped efforts utilizing agentic frameworks, such as langgraph/langchain. Experience in cybersecurity or IT B2B preferred. #J-18808-Ljbffr