Staff Software Engineer, Applied AI at Hackerone · LumiJobs
senior
$200k–$256k
H1B: no
Hackerone Staff Software Engineer, Applied AI at HackerOne to architect AI-driven systems for autonomous security agents. Drive integration of advanced AI and agentic frameworks into vulnerability discovery products.
Responsibilities
At HackerOne, we embrace a Flexible Work approach that gives us the freedom to do our best work while also fostering the connections and community that make us stronger. Reflecting this philosophy, this is a role targeted for candidates within ~50 miles of Seattle, WA. We believe this balance of proximity and flexibility gives Hackeronies the chance to occasionally come together – fostering collaboration, connection, and in-person moments that enrich our culture – while still preserving the benefits of remote work. Must be able and willing to come to the office once per week (typically Thursdays).
Primary Responsibilities
Success in the Staff AI Engineer role will be accomplished by delivering on the responsibilities below in alignment with the Talent Principles that define how we work at HackerOne.
Architect and enhance our autonomous security agent “Hai,” building intelligent systems capable of natural-language reasoning, vulnerability detection, and actionable recommendations, all grounded in an AI-First mindset.
Build components and services that integrate agentic AI design patterns—such as orchestration, memory systems, RAG, long-horizon tasks, and LLM-based models—into the HackerOne platform, applying an AI-First approach to improve vulnerability detection and security automation.
Partner across Product, Security Research, and Engineering to introduce AI capabilities into the broader HackerOne ecosystem. As the company evolves rapidly, bring clarity and stability to shifting requirements by demonstrating strong Change Agility.
Design and implement AI red-teaming agents and frameworks that proactively surface weaknesses in LLMs, generative-AI systems, and applied AI deployments, using First Principles Problem Solving to break problems down and build durable, foundational solutions.
Establish meaningful metrics, observability, evaluation frameworks, and continuous feedback loops to improve model performance, safety, and user impact—ensuring decisions are grounded in Data-Driven Decision Making.
Stay current with emerging AI safety research, adversarial-testing techniques, and agentic-system patterns, and integrate those learnings into HackerOne’s responsible-AI strategy with adaptability and a growth-oriented Change Agility mindset.
Build APIs and integrations that enable seamless interaction between AI models, security tools, and the broader HackerOne platform, ensuring security, scalability, and interoperability across systems.
Qualification
Familiarity with ReActExperience in agent action routingKnowledge of prompt engineeringFamiliarity with Ruby on RailsHealth (medicalEquity stock options
Required
8+ years of experience as a software engineer, including deep experience building and maintaining production-grade AI platforms and infrastructure.
Must be able and willing to come to the office once per week (typically Thursdays).
Proven expertise in large language models (LLMs), generative AI, and machine learning frameworks such as TensorFlow, PyTorch, and Transformers in production environments.
Strong hands-on experience in AI platform engineering, including model deployment, MLOps pipelines, model serving infrastructure, and shared AI services architecture.
Experience building systems that support multiple AI product teams and applications, enabling scalable experimentation and deployment.
Solid understanding of AI safety and alignment principles, including responsible AI development, bias mitigation, and ethical AI practices.
Preferred
Experience building AI development platforms, model registries, experimentation frameworks, and tools that accelerate AI innovation across organizations.
Familiarity with ReAct, AutoGen, or Semantic Kernel for agentic orchestration and multi-agent collaboration.
Experience in agent action routing, secure tool usage APIs, and feedback loops for autonomous agents.
Knowledge of prompt engineering, fine-tuning, retrieval-augmented generation (RAG), and advanced LLM optimization strategies.
Experience with cloud-based AI/ML services (AWS Bedrock, GCP Vertex AI, Azure ML) and containerization technologies (Docker, Kubernetes) for AI workloads.
Familiarity with Ruby on Rails, GraphQL, and React, and experience integrating AI capabilities into production web applications and APIs.
Health (medical, vision, dental), life, and disability insurance*
