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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.
Integrated Marketing Campaign Lead
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Remote Locations: Seattle, WA; Boston, MA; Austin, TX; or Washington, DC
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Position Summary
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HackerOne is hiring an Integrated Campaign Marketing Manager to join the Growth team, reporting to the Senior Director of Brand and Growth Revenue.
Integrated campaigns require a blend of creativity, collaboration, and operational rigor. This role will own the development and execution of the campaign framework that connects HackerOne’s brand narrative to demand generation across growth, field, and cross\-functional marketing programs. The right person will bring structure, creativity, and data\-driven precision to campaign planning and execution, helping translate our messaging and market point of view into programs that drive engagement, pipeline, and revenue.
This role is focused on building and running integrated campaigns that can be activated across channels, teams, audiences, and geographies, while creating stronger alignment between brand storytelling and business results.
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 Seattle, WA; Boston, MA; Austin, TX; or Washington, DC. 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
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### Campaign Strategy \& Planning
- Develop and execute multi\-channel marketing campaigns that drive demand, pipeline growth, and brand awareness across target audiences and geographies
- Build campaign plans that connect audience, messaging, content, offers, channel mix, and measurement
- Create campaign briefs, goals, target audience definitions, KPIs, and testing plans
- Define and evolve scalable campaign themes and programs that can run across multiple quarters
### Campaign Execution \& Management
- Manage campaign launches from setup through optimization, ensuring strong execution and on\-time delivery
- Coordinate campaign assets, timelines, and cross\-functional workflows across marketing and go\-to\-market teams
- Own end\-to\-end channel execution across digital, content, lifecycle, field, and sales\-touch programs
- Oversee campaign tracking and measurement to ensure clear visibility into performance
### Cross\-Functional Alignment
- Lead cross\-functional alignment with Brand, Product Marketing, Growth, Demand, Field, Sales, SDR, and Regional Marketing teams to align campaigns with business priorities
- Help ensure campaigns are rooted in a clear, differentiated narrative and translated effectively into market\-facing programs
- Collaborate with sales and go\-to\-market teams to support campaign follow\-up, activation, and enablement
- Contribute to scalable campaign playbooks and templates that can be used across teams and regions
### Reporting \& Analysis
- Conduct ongoing campaign reporting and share actionable insights and recommendations
- Partner with marketing ops and revenue stakeholders to measure performance across lead, conversion, pipeline, and ROI metrics
- Identify opportunities to improve campaign effectiveness through audience, messaging, channel, and offer optimization
- Support a test\-and\-learn approach that drives continuous improvement in campaign performance
Minimum Qualifications
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- 5\+ years of experience in B2B marketing, with a strong foundation in campaign management, demand generation, growth marketing, or integrated marketing. Cybersecurity or AI technology experience is a plus.
- Experience supporting or managing multi\-channel campaigns across a range of audiences, segments, and channels
- Strong analytical, project management, and cross\-functional collaboration skills, with the ability to turn strategy into execution
- Familiarity with marketing automation, CRM, and project management tools such as Salesforce, Marketo, HubSpot, Monday.com, or Asana
- Experience using AI tools (e.g., ChatGPT) to enhance marketing workflows, content creation, campaign execution, or analysis
Compensation Bands:
Tier Guide
Tier B: $129K\-$144k Base
Offers Equity
\#LI\-Remote
\#LI\-KM1
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: $129K \- $144K
Salary Context
This $129K-$144K range is above the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% 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 $166,983 based on 13,781 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($136K) sits 18% below the category median. Disclosed range: $129K to $144K.
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
AI roles in Austin pay a median of $212,800 across 317 tracked positions. That's 16% above the national 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 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).
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 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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