Software Engineer, Applied AI

Primary Responsibilities

Success in the AI Engineer role will be accomplished by delivering on the responsibilities below in alignment with the Values and Talent Principles that define how we work at HackerOne.

• Apply an AI First mindset by building and integrating AI capabilities such as LLM-powered workflows, RAG pipelines, or orchestration components that enhance vulnerability detection and security automation.
• Demonstrate First Principles Problem Solving by breaking down AI and security challenges into clear components, identifying core assumptions, and implementing simple, maintainable solutions.
• Use Data-Driven Decision Making to evaluate model performance, test hypotheses, analyze telemetry, and iterate on features based on measurable impact.
• Practice Change Agility by adapting to evolving AI frameworks, product requirements, and security considerations while maintaining delivery momentum.
• Contribute to the development of our AI security agent, Hai, implementing features that enable natural language insights, reasoning workflows, and secure model interactions.
• Build and maintain APIs and services that enable secure interactions between AI models, internal systems, and third-party platforms.
• Partner cross-functionally with Product, Security Research, and Customer Success to clarify requirements, translate user needs into technical tasks, and deliver reliable solutions.
• Stay current with emerging AI and AI security trends, sharing learnings with the team and incorporating relevant advancements into your work.

Minimum Qualifications

• 3+ years of experience as a software engineer, with hands-on experience building production systems
• Experience integrating LLMs or generative AI models into applications

Hands-on experience with ML frameworks such as PyTorch, TensorFlow, or HuggingFace Transformers

• Must be able and willing to come to the office once per week (typically Thursdays)

Preferred Qualifications

• Familiarity with agentic frameworks such as ReAct, AutoGen, LangChain, or Semantic Kernel
• Experience with RAG architectures, prompt engineering, fine-tuning, or LLM evaluation techniques
• Exposure to cloud AI/ML services (AWS Bedrock, GCP Vertex AI, Azure ML)
• Familiarity with full-stack technologies such as Ruby on Rails, GraphQL, or React for integrating AI features into production systems

Compensation Bands

Seattle, WA; Boston, MA; Washington, DC; or Austin, TX$166K – $203K • Offers Equity

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