Interested in this AI/ML Engineer role at HackerOne?
Apply Now →Skills & Technologies
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.
Director, AI Product Design
===============================
Remote Location: Boston, MA; Seattle, WA; San Francisco Bay Area; Austin, TX; Washington, DC; or London, UK
---------------------------------------------------------------------------------------------------------------
Position Summary
--------------------
HackerOne is looking for a Director, Product Design to lead and scale our design function as we build the next generation of AI\-native security products. In this role, you will guide a team of designers responsible for shaping intuitive, high\-impact experiences across the HackerOne platform while helping organizations discover and remediate vulnerabilities faster and more effectively.
Reporting to the Chief Product Officer, you will play a central role in defining how design evolves in a world where software creation is becoming increasingly AI\-driven. You will help inspire and lead the organization toward a design\-first mindset—one where design thinking becomes a core pillar of how complex systems are imagined, built, and operated.
As HackerOne evolves its platform around Continuous Threat Exposure Management (CTEM), you will help define the next generation of human\-computer interaction, where intelligent agents increasingly act as the primary workforce. In this future, systems will be designed for agent\-to\-agent interaction at scale, while humans serve as trusted partners providing oversight, accountability, and strategic decision\-making. Your work will also focus on designing visualization and interaction models that simplify complex workflows, enable confident decision\-making, and make advanced security workflows accessible to both machines and humans.
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 remote role targeted for candidates within \~50 miles of Boston, MA; Seattle, WA; San Francisco Bay Area; Austin, TX; Washington, DC; or London, UK. 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.
What You Will Do
--------------------
Success in the Director, Product Design role will be accomplished by delivering on the responsibilities below in alignment with the principles that define how we work at HackerOne.
- Inspire and guide the organization toward a design\-first mindset, helping teams embrace design thinking as a core discipline in building complex AI\-native systems and shaping the future of HackerOne’s platform.
- Lead and develop a team of product designers, creating the systems, processes, and leadership culture that Multiply Your Impact across the product organization while enabling designers to deliver high\-quality experiences at scale.
- Partner with Product, Engineering, and executive leadership to define the long\-term vision for how design shapes AI\-native security platforms, ensuring teams consistently Own the Outcome for product usability, clarity, and customer impact.
- Champion an AI First approach to product design by creating interaction models, workflows, and experiences where intelligent agents perform the majority of operational work while humans provide trust, oversight, and strategic decision\-making.
- Design the future of human\-computer interaction in HackerOne platform by defining patterns for agent\-to\-agent interactions, human\-in\-the\-loop oversight, and transparent AI systems that support trust and accountability.
- Lead the development of visualization strategies that simplify complex security use cases, enabling users to understand risk, prioritize actions, and make informed decisions across highly complex workflows.
- Apply First Principles Problem Solving to rethink traditional security workflows, designing systems that simplify complexity and unlock new ways for humans and AI agents to collaborate effectively.
- Use Data\-Driven Decision Making to guide design priorities, evaluate product experience outcomes, and continuously improve usability, adoption, and platform effectiveness.
- Establish and scale a product design system that enables consistent user experiences, accelerates product development, and supports the evolution of AI\-native product interactions across the HackerOne platform.
Minimum Qualifications
--------------------------
- 10\+ years of experience in product design, including leadership roles within B2B SaaS product organizations
- Demonstrated experience leading and developing high\-performing product design teams
- Experience designing AI\-enabled or AI\-native product experiences, including human\-in\-the\-loop workflows, automation systems, or intelligent decision\-support tools
- Experience scaling design functions within high\-growth SaaS environments
- Proven success building or scaling product design systems for complex software platforms
Preferred Qualifications
----------------------------
- Experience designing products for developer platforms, security platforms, or technical user audiences
- Background working in the cybersecurity or security research ecosystem
- Experience designing AI\-assisted workflows that combine automation with expert human decision\-making
- Background in Human–Computer Interaction (HCI) or experience designing interaction models for complex systems, human\-AI collaboration, or agent\-driven workflows
Compensation Bands:
San Francisco
$270K – $300K • Offers Equity
Seattle, Austin, Boston, DC
-------------------------------
$243K – $297K • Offers Equity
London, UK
--------------
$137K – $168K • Offers Equity
*\#LI\-remote*
Job Benefits:
-----------------
- 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: $243K \- $300K
Salary Context
This $243K-$300K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1937 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At HackerOne, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $181,170 based on 12,692 positions with disclosed compensation. Director-level AI roles across all categories have a median of $247,800. This role's midpoint ($271K) sits 50% above the category median. Disclosed range: $243K to $300K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
HackerOne AI Hiring
HackerOne has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Washington, DC, US. Compensation range: $300K - $300K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 $275,000 median, while Prompt Engineer roles sit at $140,000. 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: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.