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About This Role
Job Title: Senior AI Search & SEO Strategist
About Gen
Gen is a global company dedicated to powering Digital Freedom through its trusted consumer brands including Norton, Avast, LifeLock, MoneyLion and more. Our combined heritage is rooted in financial empowerment and Cyber Safety for the first digital generations, and today we deliver award-winning cybersecurity, online privacy, identity protection, and financial wellness solutions to nearly 500 million users in more than 150 countries.
Together, we share a collective passion and vision to protect consumers and help them grow, manage, and secure their digital and financial lives. We’re always looking for smart, fearless, high-impact talent who see AI as a teammate, leveraging it to move faster and deliver meaningful results.
When you’re part of Gen, you’ll have the flexibility, tools, and support to do your best work and grow your career, from flexible working options and time off to competitive pay, benefits, and well-being programs.
At Gen, we are scrappy and relentlessly customer driven. We create room for healthy debate, experimentation, and continuous learning, and we seek out people with different experiences, identities, and ideas to join our team. You’ll work with people who back each other, respect each other, and understand that our differences are a competitive advantage.
If this sounds like you, we’d love you to be part of Gen.
How We Work
Our hybrid work model (3 days in the New York office) gives us the face-to-face time to have creative conversations, hold meaningful meetings, make quick decisions, and build relationships. At the same time, it offers the flexibility you need to focus and do your best work.
About the Role
The Senior AI Search & SEO Strategist is a key practitioner on Gen's Discoverability and Distribution team, responsible for driving visibility and authority of Gen's consumer brands across organic search, AI-powered discovery surfaces, and the open web.
You'll manage the content optimization strategy of our cybersecurity and identity portfolio, shaping how Gen's websites and brands appear at a time when search systems, large language models, and AI answer engines are fundamentally changing how consumers discover information.
This is not a traditional organic optimization position; this is a role for a hands-on, high-impact expert equally fluent in SEO craft, content strategy, agentic workflows, and the emerging field of Answer Engine Optimization (AEO). Success depends on both understanding how LLMs synthesize and surface information and being able to create and communicate strategy effectively to build the strong cross-functional relationships required to execute.
You’ll join a team of digital content strategists, technical-optimization experts, editors, and web publishers. And you’ll work closely with PR, affiliate, product, and other internal teams — helping connect organic and AEO content strategy to brand visibility and customer acquisition goals.
We are a collaborative, globally distributed team that moves quickly, shares knowledge openly, and takes the quality of our work seriously without taking ourselves too seriously.
Key Responsibilities
Cross-functional Collaboration
- Partner with technical SEO, web development, UX, and product teams to ensure content strategy and execution align with organic and AI Search best practices.
- Identify and own new brand-visibility and audience-growth projects, earning support from other teams and communicating value and results to leadership.
- Serve as the primary organic and AEO POC for internal and external partners - providing briefs and reviewing deliverables for web development projects, redesigns, system migrations, and other projects to ensure standards are met.
- Champion SEO and AEO best practices throughout the organization, evangelizing standards across marketing, web production, product, and other internal teams.
Content Strategy & Optimization
- Manage the organic and acquisition content strategy for Gen's cybersecurity portfolio.
- Oversee content audits, URL mapping exercises, and refresh workflows to ensure clear content architecture, product-roadmap alignment, and strong organic and web performance.
- Develop detailed content briefs that guide editorial and web teams to create best-in-class landing pages.
Monitoring & Reporting
- Work with enterprise and custom analytics tools to track and analyze web performance and LLM visibility, using data to drive optimization decisions and translate findings into editorial and digital PR activation plans.
- Own the team's monthly and quarterly SEO/AEO reporting, measuring and communicating AEO onsite and offsite initiatives and providing context and insights to inform strategy.
About You
You’ll bring craft, curiosity, conscientiousness, speed, and rigor, raising the bar for how our team helps our brands communicate core values, meet audiences, and build consumer trust.
- 5+ years of hands-on SEO (and AEO) experience with a strong focus on content strategy, editorial optimization, and organic performance — ideally in a tech or YMYL vertical.
- Proven track record of driving measurable organic and AI search improvements through content strategy and optimization.
- Experience creating content briefs and working with freelance writers or agency partners to ensure exceptional content execution.
- Strong analytical skills, comfortable with performance data, and able to translate insights into prioritized action plans.
- Familiarity with AEO, entity SEO, and experience optimizing content for AI-powered search surfaces.
- Deep proficiency with SEO tooling: Semrush, Ahrefs, Google Search Console, Screaming Frog, and/or equivalent platforms.
- Proficiency with Google Analytics, Adobe Analytics, Amplitude, or other analytics platforms. Ad hoc dashboard creation in Looker Studio, Power BI, or a similar tool a plus.
- Growing fluency with (or eagerness to master) emerging enterprise LLM monitoring tools like Profound.
- Strong experience building projects and agents with Claude or ChatGPT, while remaining conscientious and capable of distinguishing signals from noise.
- Experience applying E-E-A-T and YMYL content quality standards, preferably in a rigorous editorial environment.
- Comfortable managing multiple concurrent workstreams across external agencies, internal teams, and cross-functional partners.
- Exceptional written and verbal communication skills; ability to translate complex SEO/AEO/digital marketing concepts to non-specialists and influence stakeholder decision making.
- Experience with enterprise or global/multi-brand website environments a plus.
- A positive, adaptable, and collaborative approach, able to operate effectively and flexibly without supervision.
- Highly organized and detail-oriented with an obsessive focus on results, quality, and analytical precision.
Compensation
The annual base salary for this position is $165,000 USD. This role is also eligible for an annual performance-based bonus. Base salary is one component of Gen’s total compensation package. Actual compensation will vary based on a candidate’s qualifications, experience, skills and competencies related to the role.
Benefits
- 401(k) retirement plan with company match: Gen matches 50% of the first 7% of eligible pay you contribute, capped at $6,000 per year in company match.
- Health Savings Account (HSA) funding: If you enroll in an HSA-eligible medical plan, Gen contributes $500–$1,500 per year depending on your coverage tier.
- Wellness and fitness reimbursement: Up to $375 per year for eligible fitness and wellness expenses.
- Sustainable home improvement allowance: Up to $500 per year for approved sustainable home upgrades.
- Tuition and education reimbursement: Up to $5,250 per calendar year for approved, job-related education.
What’s Next
- If this sounds like the right fit for you, we want to hear from you. Here’s what to expect after you apply:
- Application Review: Our team will review your application and reach out within 2 weeks if we want to move forward.
- Introductory Zoom meeting with Recruiter: A 15-20-minute conversation with a recruiter to learn more about your background, talk about the role, find out where you can add value quickly, and why you are compelled to work with (Norton, MoneyLion, LifeLock, Avast)
- Hiring Manager Interviews: Deeper conversations with the hiring manager on your experience, the scope of the role, what success means, etc.
- Team Interviews: Conversations with other members of the team.
- Final Decision: We move quickly and will keep you informed at every step.
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Gen is an equal opportunity employer, and we’re committed to fair, inclusive practices at every stage of the candidate and employee journey. Employment decisions are based on merit, experience and business needs.
Compensation Range: $133.6K - $178.5K
Salary Context
This $133K-$178K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Gen Digital Inc., 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 $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($156K) sits 26% below the category median. Disclosed range: $133K to $178K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Gen Digital Inc. AI Hiring
Gen Digital Inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $178K - $178K.
Location Context
AI roles in New York pay a median of $221,000 across 2,878 tracked positions. That's 4% 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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