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About This Role
Who are we?
Smarsh empowers its customers to manage risk and unleash intelligence in their digital communications. Our growing community of over 6500 organizations in regulated industries counts on Smarsh every day to help them spot compliance, legal or reputational risks in 80\+ communication channels before those risks become regulatory fines or headlines. Relentless innovation has fueled our journey to consistent leadership recognition from analysts like Gartner and Forrester, and our sustained, aggressive growth has landed Smarsh in the annual Inc. 5000 list of fastest\-growing American companies since 2008\. Summary
As our Generative AI \& SEO Strategist, you will lead optimization across AI\-powered search experiences — including Google AI Overviews (AIO), Generative Engine Optimization (GEO), and AI Index Engine Optimization (AIEO). This role is critical to ensure Smarsh remains discoverable and competitive as the search landscape transforms.
You will blend deep SEO expertise with advanced AI search strategies to maximize our visibility, protect and grow organic\-driven marketing\-qualified leads (MQLs), and directly influence our sales pipeline.
### How will you contribute?
- Protect and grow search\-driven pipeline by optimizing and implementing GEO and SEO strategy.
- Monitor and grow Smarsh's brand and product visibility across LLM\-powered platforms including ChatGPT, Perplexity, Google AI Overviews, and emerging entrants using the Profound AEO platform.
- Own and execute Smarsh's global SEO, GEO, and AI search strategy to increase organic visibility, AI citation presence, and pipeline contribution across digital channels.
- Optimize digital experiences for both traditional search engines and AI\-powered discovery platforms including ChatGPT, Gemini, Perplexity, Copilot, and Google AI Overviews.
- Lead keyword, topic cluster, and entity\-based research to expand content authority and improve discoverability across traditional search and AI ecosystems.
- Build and maintain prompt libraries across all product categories to systematically test and track citation performance.
- Analyze citation share data to identify where we are and are not appearing, and build a prioritized action plan to close gaps.
- Conduct AI query intent analysis and adapt content strategy to match high\-intent, AI\-surfaced search terms.
- Advise on semantic HTML, schema, and entity optimization to improve AI summarization and citation rates.
- Deliver technical SEO recommendations that improve crawlability, indexing, site architecture, structured data implementation, and Core Web Vitals performance.
- Design and implement internal linking, on\-page optimization, and content structuring strategies that improve engagement, discoverability, and search performance.
- Regularly audit website performance for AI\-readability, structured data accuracy, and zero\-click search inclusion.
- Build and monitor multi\-platform search intelligence dashboards, tracking presence in AI\-generated results alongside traditional rankings.
- Build measurement frameworks that quantify AI search visibility, answer engine performance, and overall GEO effectiveness.
- Partner with content, product marketing, demand generation, and regional marketing teams to ensure all new and existing content is optimized for SEO and AI search.
- Conduct ongoing competitive intelligence across traditional search and AI search platforms to identify opportunities, defend market share, and improve share of voice.
- Develop and execute structured SEO and GEO experiments to improve AI citation performance and organic visibility.
- Establish SEO and AI optimization standards and best practices across digital content and web experiences.
- Champion adoption of AI search best practices through cross\-functional enablement and documentation.
- Identify opportunities to automate SEO analysis, AI citation monitoring, reporting, and optimization workflows using AI technologies.
- Partner with Revenue Marketing and Operations to connect SEO and AI initiatives to pipeline and revenue attribution.
- Drive initiatives that strengthen content authority, trust signals, entity relationships, and E\-E\-A\-T to improve AI and traditional search visibility.
- Support international SEO initiatives including regional content strategies and hreflang recommendations as global expansion evolves.
- Communicate search insights and strategic recommendations to executive stakeholders, influencing priorities that drive global digital growth.
- Produce high\-quality, data\-driven reporting on AI search visibility, traffic, and conversion metrics.
### What will you bring?
- Bachelor's degree in marketing, digital strategy, or related field.
- 5\+ years of experience in B2B search engine optimization; SaaS and financial services experience preferred.
- Proven record of driving measurable pipeline impact from organic and AI\-optimized search strategies.
- Deep understanding of Generative Engine Optimization (GEO), Answer Engine Optimization (AEO), AI Index Engine Optimization (AIEO), and evolving AI\-powered search behaviors.
- Hands\-on experience with enterprise SEO platforms including Profound, GA4, Google Search Console, SEMrush, Ahrefs, Screaming Frog, or equivalent technologies.
- Strong knowledge of structured data markup (Schema.org) and entity optimization for AI discoverability.
- Skilled in analyzing AI\-generated search outputs and reverse\-engineering strategies to secure placement.
- Proficiency in HTML, CSS, and familiarity with JavaScript implementation best practices.
- Demonstrated ability to translate complex technical findings into clear, actionable recommendations for executive and cross\-functional stakeholders.
- Proven ability to influence cross\-functional teams and align web, content, product marketing, regional marketing, and development teams around a unified search strategy.
- Highly analytical and results\-driven, with the ability to tie visibility metrics directly to lead generation and revenue impact.
- Experience with WordPress CMS and enterprise\-level website optimization.
- Self\-motivated, collaborative, and able to lead cross\-functional optimization initiatives.
The above salary range represents Smarsh's good faith and reasonable estimate of the range of possible base compensation at the time of posting.
Any applicable bonus programs will be discussed during the recruiting process.
The salary for this role will be set based on a variety of factors, including but not limited to, internal equity, experience, education, location, specialty and training. Local cost of living assessments are done for each new hire at the time of offer.
About our culture
Smarsh hires lifelong learners with a passion for innovating with purpose, humility and humor. Collaboration is at the heart of everything we do. We work closely with the most popular communications platforms and the world’s leading cloud infrastructure platforms. We use the latest in AI/ML technology to help our customers break new ground at scale. We are a global organization that values diversity, and we believe that providing opportunities for everyone to be their authentic self is key to our success. Smarsh leadership, culture, and commitment to developing our people have all garnered Comparably.com Best Places to Work Awards. Come join us and find out what the best work of your career looks like.
Salary Context
This $100K-$110K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Smarsh, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($105K) sits 51% below the category median. Disclosed range: $100K to $110K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Smarsh AI Hiring
Smarsh has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Atlanta, GA, US, Remote, US. Compensation range: $110K - $240K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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