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About This Role
HackerOne is a global leader in Continuous Threat Exposure Management (CTEM). The HackerOne Platform unites agentic AI solutions with the ingenuity of the world’s largest community of security researchers to continuously discover, validate, prioritize, and remediate exposures across code, cloud, and AI systems. Through solutions like bug bounty, vulnerability disclosure, agentic pentesting, AI red teaming, and code security, HackerOne delivers measurable, continuous reduction of cyber risk for enterprises. Industry leaders, including Anthropic, Crypto.com, General Motors, Goldman Sachs, Lufthansa, Uber, UK Ministry of Defence, and the U.S. Department of Defense, trust HackerOne to safeguard their digital ecosystems. HackerOne was recognized in Gartner’s Emerging Tech Impact Radar: AI Cybersecurity Ecosystem report for its leadership in AI Security Testing and has been named a Most Loved Workplace for Young Professionals (2024\).
HackerOne is at a pivotal inflection point in the security industry. Offensive security is no longer optional – it is the standard for forward\-thinking companies that want to build trust and resilience in a world where AI\-driven innovation and adversaries are moving faster than ever. With the industry shifting, HackerOne stands apart: we combine the ingenuity of the largest security research community with a best\-in\-class AI\-powered platform, trusted by the world’s top organizations.
HackerOne Values
HackerOne is dedicated to fostering a strong and inclusive culture. HackerOne is Customer Obsessed and prioritizes customer outcomes in our decisions and actions. We Default to Disclosure by operating with transparency and integrity, ensuring trust and accountability. Employees, researchers, customers, and partners Win Together by fostering empowerment, inclusion, respect, and accountability.
Senior Software Engineer, Applied AI
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Location: Seattle, WA; Austin, TX; Boston, MA; Washington, DC
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1 day onsite per week
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Position Summary
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At HackerOne, we’re revolutionizing offensive security by combining human intelligence with artificial intelligence to help organizations build a safer internet. As a Senior Software Engineer on our AI Platform team, you will contribute directly to the development of our next\-generation AI security capabilities, including our in\-platform AI security agent, Hai. You will help develop AI\-powered features that enhance vulnerability discovery, improve security analysis workflows, and expand how thousands of customers detect and respond to emerging threats.
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 , Boston, Washington DC, or Austin, TX. 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).
What You Will Do
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Success in this role will be accomplished by delivering the responsibilities below in alignment with HackerOne’s Talent Principles: AI\-First, Change Agility, Data\-Driven Decision Making, and First Principles Problem Solving.
- Contribute to evolving our AI security agent Hai, implementing features that enable natural language security insights, improved reasoning, and secure action workflows.
- Build components and services that integrate Agentic AI design patterns like AI Orchestration, Memory systems, RAG, Long horizon tasks, LLM\-based models into the HackerOne platform, applying an AI\-First approach to enhance vulnerability detection and security automation.
- Implement features for AI red teaming agents and evaluation tools that help identify risks in LLMs and applied AI systems, supporting safer model development.
- Collaborate cross\-functionally with Product, Security Research, and Customer Success to translate customer needs into clear engineering requirements and deliverables.
- Develop and integrate APIs that enable secure interactions between AI models, internal services, and third\-party systems, adjusting quickly as requirements evolve with Change Agility.
- Use metrics, evaluation data, and telemetry to validate model behavior and improve system performance in alignment with Data\-Driven Decision Making.
- Break down complex AI and security problems into smaller, fundamental components, applying First Principles Problem Solving to deliver simple, scalable solutions.
- Stay current with emerging AI security trends, contributing findings and learnings to team discussions and implementation planning.
Minimum Qualifications
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- 5\+ years of experience as a software engineer
- Must be able and willing to come to the office once per week (typically Thursdays)
- Experience implementing features within production\-grade AI or ML systems, including integrating LLMs or generative AI models into applications
- Hands\-on experience with ML frameworks such as PyTorch, TensorFlow, or HuggingFace Transformers
- Familiarity with model deployment workflows, evaluation techniques, or MLOps concepts
- Understanding of responsible AI development and basic AI safety considerations
Preferred Qualifications
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- Experience contributing to AI development platforms, internal ML tooling, or experimentation workflows
- Exposure to agentic frameworks such as ReAct, AutoGen, LangChain, or Semantic Kernel
- Familiarity with prompt engineering, fine\-tuning, RAG architectures, or LLM optimization techniques
- Experience working with cloud AI/ML services (AWS Bedrock, GCP Vertex AI, Azure ML)
- Familiarity with full\-stack technologies such as Ruby on Rails, GraphQL, or React—especially for integrating AI capabilities into existing systems
Compensation Bands:
Seattle, WA , Boston, Washington DC, or Austin, TX
$190K – $230K • Offers Equity Options
\#LI\-HM1
Job Benefits:
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- Health (medical, vision, dental), life, and disability insurance\*
- Equity stock options
- Retirement plans
- Paid public holidays and unlimited PTO
- Paid maternity and parental leave
- Leaves of absence (including caregiver leave and leave under CO's Healthy Families and Workplaces Act)
- Employee Assistance Program
- Eligibility may differ by country
We're committed to building a global team! For certain roles outside the United States, India, the U.K., and the Netherlands, we partner with Remote.com as our Employer of Record (EOR).
Visa/work permit sponsorship is not available.
*Employment at HackerOne is contingent on a background check.*
HackerOne is an Equal Opportunity Employer in the terms and conditions of employment for all employees and job applicants without regard to race, color, religion, sex, sexual orientation, age, gender identity or gender expression, national origin, pregnancy, disability or veteran status, or any other protected characteristic as outlined by international, federal, state, or local laws.
This policy applies to all HackerOne employment practices, including hiring, recruiting, promotion, termination, layoff, recall, leave of absence, compensation, benefits, training, and apprenticeship. HackerOne makes hiring decisions based solely on qualifications, merit, and business needs at the time.
For US based roles only: Pursuant to the San Francisco Fair Chance Ordinance, all qualified applicants with arrest and conviction records will be considered for the position.
Compensation Range: $190K \- $230K
Salary Context
This $190K-$230K range is above the median for AI Software Engineer roles in our dataset (median: $189K across 518 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 26,159 AI roles we're tracking, AI Software Engineer positions make up 2% of the market. At HackerOne, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $235,100 based on 665 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($210K) sits 11% below the category median. Disclosed range: $190K to $230K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
HackerOne AI Hiring
HackerOne has 12 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Positions span Austin, TX, US, Washington, DC, US, Seattle, WA, US. Compensation range: $144K - $280K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 median).
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
AI Hiring Overview
The AI job market has 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 roles).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
The AI Job Market Today
The AI job market spans 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (2,416) are outnumbered by mid-level (16,247) and senior (5,153) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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