Interested in this AI/ML Engineer role at RSA Conference?
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About This Role
RSAC is the premier series of global events and year‑round learning for the cybersecurity community. RSAC is where the security industry converges to discuss current and future concerns and gain access to experts, unbiased content, and ideas that help individuals and companies advance their cybersecurity posture and build stronger, smarter teams.
Both in‑person and online, RSAC brings the cybersecurity industry together and empowers the collective “we” to stand against cyberthreats around the world. RSAC is the ultimate marketplace for the latest technologies and hands‑on educational opportunities, helping industry professionals discover how to make their organisations more secure while showcasing the most enterprising, influential, and thought‑provoking thinkers and leaders in cybersecurity today.
We're looking for a skilled Email \& Web Specialistto own the technical execution of our digital marketing presence. You'll split your time between building and deploying email programs in HubSpot and developing website pages—making sure every touchpoint is polished, on\-brand, and performing. This is a hands\-on role for someone who loves living at the intersection of marketing and technology. You’ll work closely with the Email Marketing Manager and Website Marketing Manager.
What You’ll Do
Email Development (HubSpot)* Build emails in HubSpot using established templates while ensuring content, links, and formatting are accurate before deployment
- Schedule and deploy email campaigns, including managing send times and audience lists/segmentation within HubSpot
- Implement personalization tokens, smart content, and dynamic fields to improve relevance and engagement
- Conduct A/B tests on subject lines, layouts, and CTAs; analyze results and apply learnings
- Maintain list segmentation and ensure compliance with CAN\-SPAM and GDPR best practices
- QA emails thoroughly before deployment across devices and email clients
Website Development* Build and maintain landing pages, website pages, and content components within the CMS (expertise in Sitecore and/or Liferay strongly preferred but willingness to learn is a must)
- Collaborate with designers to translate mockups and brand guidelines into polished, responsive web pages using HTML, CSS, and JavaScript
- Work within CMS platform component system to create and update page templates and renderings
- Partner with back\-end developers or a development team as needed to implement new functionality
- Optimize pages for performance, SEO, and conversion
- Support campaign launches by building dedicated landing pages and thank you pages
General* Assist Website Marketing Manager in monitoring and reporting on email and web performance metrics; surface insights and recommendations
- Stay current on HubSpot platform updates, CMS best practices, and web standards
Application Requirements
Core Requirements* 3–5 years of experience in a marketing development, email development, and/or web development role
- Hands\-on proficiency with HubSpot Marketing Hub (HubSpot certifications a plus)
- Experience building and managing pages in Sitecore and/or Liferay CMS
- Solid command of HTML and CSS for email; familiarity with email rendering quirks across clients
- Working knowledge of front\-end web technologies: HTML, CSS, JavaScript
- Experience building responsive, accessible web pages and emails
- Familiarity with marketing automation concepts: workflows, lifecycle stages, lead scoring is a plus
Bonus Experience* Experience with Litmus for email testing
- Familiarity with Sitecore's personalization or analytics features
- Familiarity with SEO best practices and tools (SEMrush, Google Search Console, etc.)
- Basic understanding of HubSpot CRM and contact/list management
- Experience working with a design system or component library
- Knowledge of accessibility standards (WCAG 2\.1\)
Location \& Eligibility
Please be aware that although this is a remote position, to be considered for the vacancy you must have residency in one of the following states:
California, Colorado, Florida, Kansas, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Texas, Utah, Virginia, Washington
Benefits
RSAC believes in investing in our people. We offer:* Salary range: $90,000 – $110,000
- Employer‑subsidised medical, dental, and vision insurance
- 401K retirement employer match
- Home office equipment stipend and monthly technology stipend
- Thirteen paid holidays per calendar year
- Flexible personal time off
- Annual employee bonus dependent on company and personal performance
- Annual company‑wide offsite
Hiring Process
Our interview process includes an initial screening conversation, followed by a series of conversations with the hiring manager and team. We aim to keep things efficient and give you a clear understanding of the role, team, and how we work.
Our Culture
We believe that our differences make us stronger, and we are committed to fostering a culture of respect, empathy, and understanding.* We are a fully remote team operating across the United States, giving our employees the flexibility to work from wherever they choose.
- Our team is passionate and results‑oriented, striving to achieve excellence in everything we do.
- We strongly believe in creating an inclusive environment that values diversity and encourages our team members to share their unique perspectives.
- We recognise that by collaborating and working together, we can achieve our goals faster and more effectively.
Why RSAC?
The RSAC team takes great pride in helping shape the future of cybersecurity and being part of an expansive global community. We’re always looking for imaginative and visionary individuals who share our passion for providing cutting‑edge programs that equip cybersecurity practitioners worldwide with the intel and knowledge they need to thrive and safeguard organisations against cyberthreats.
Our Values* Adaptability: In our ever‑changing world, we innovate through determination, creativity, and resourcefulness.
- Community: We bring people together and build trust by embracing the unique thoughts and perspectives of others with kindness and respect.
- Excellence: Because we are where the world talks security, we have the highest expectations of ourselves and our partners.
Equal Opportunity Statement
RSAC is an equal opportunity employer committed to inclusion and diversity. We take affirmative action to ensure all qualified applicants receive consideration for employment without regard to race, colour, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or other legally protected characteristics.
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Salary Context
This $90K-$110K 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 RSA Conference, 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. Mid-level AI roles across all categories have a median of $131,300. This role's midpoint ($100K) sits 40% below the category median. Disclosed range: $90K to $110K.
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.
RSA Conference AI Hiring
RSA Conference has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $110K - $110K.
Remote Work Context
Remote AI roles pay a median of $156,000 across 1,221 positions. About 7% of all AI roles offer remote work.
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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