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About This Role
Why Socure?
Socure is building the identity trust infrastructure for the digital economy — verifying 100% of good identities in real time and stopping fraud before it starts. The mission is big, the problems are complex, and the impact is felt by businesses, governments, and millions of people every day.
We hire people who want that level of responsibility. People who move fast, think critically, act like owners, and care deeply about solving customer problems with precision. If you want predictability or narrow scope, this won’t be your place. If you want to help build the future of identity with a team that holds a high bar for itself — keep reading.
Head of Digital Marketing & AI-Driven Growth
Job Overview
The Head of Digital Marketing drives the vision, strategy, and execution of Socure's digital marketing initiatives — with AI and marketing agents at the core of how the team operates. This role leads the digital marketing team, partners with cross-functional stakeholders, and builds an agentic marketing engine that senses market signals in real time and turns them into high-impact campaigns, content, and conversions. The Head of Digital Marketing is accountable for elevating digital marketing quality, pioneering AI-driven approaches to speed and scale, and mentoring team members to work effectively alongside AI agents in pursuit of business objectives.
What you'll own
- Answer engine and organic discovery (AEO + SEO). Make Socure the answer when buyers ask AI assistants about identity verification, fraud prevention, KYC, and onboarding risk. Build the program that measures and grows our share of voice and citation rate across ChatGPT, Perplexity, Gemini, Claude, Copilot, and Google AI Overviews, alongside traditional rankings. Structure our content, data, and third-party footprint so models understand and trust us.
- Paid media wherever buyers ask questions. Run a full-funnel paid program across Google, LinkedIn, Meta, LLM Ads and programmatic, and lead Socure's early moves into emerging AI ad surfaces, including sponsored placements inside conversational assistants. Use AI for targeting, creative variation, bid strategy, and budget reallocation, and know when to override it.
- Agentic lifecycle marketing. Replace static nurture streams with agent-driven lifecycle programs that read buying signals (product usage, intent data, engagement, firmographic change) and respond with the right message, channel, and timing. Partner with Sales Development and ABM so agents hand off to humans at the right moment, with full context.
- The adaptive website. Turn socure.com into an experience that adapts by account, industry, persona, and stage, with personalized content, dynamic proof points, and conversational paths to a demo. Make the site readable and useful to AI agents and crawlers as well as people, since more buyer research will be done by agents on their behalf.
- The agentic marketing engine. Design and run the system that senses the market in real time (news, competitor moves, regulatory shifts, fraud trends) and turns it into drafted content, campaign briefs, social copy, and landing pages within hours. Build the human-in-the-loop review that keeps everything accurate, on-brand, and compliant for a regulated buyer base.
- Measurement that reflects how buying works now. Build attribution and reporting that connect AI visibility, paid, lifecycle, and web engagement to pipeline and bookings. Retire metrics that no longer mean anything and define the ones that do.
- The team. Lead and grow a team spanning paid, organic/AEO, lifecycle, and digital operations. Coach each person to run their lane as an operator of agents, with the judgment to know what to automate, what to supervise, and what to keep human.
The opportunity
What success looks like in your first year
- Socure is consistently cited in AI-assistant answers for our priority categories, with a measurable, rising share of voice against named competitors.
- Digital channels source and influence a clearly larger share of qualified pipeline, with attribution leadership trusts.
- At least one agentic lifecycle program and one signal-to-content workflow are live, producing work weekly, with documented review and quality controls.
- The website personalizes for priority accounts and segments, and demo conversion improves as a result.
- Your team ships more, faster, at higher quality, and each member can show what they've built with AI.
Who you are
You've lived at the front edge of digital marketing for the last few years and have the scars to prove it. You've run real budgets and real teams, and you've also personally built agents, prompts, automations, and workflows that run in production. You think in systems, read data fluently, and write well enough to tell good AI output from plausible AI output in seconds. You're comfortable with ambiguity because most of the playbook you need hasn't been written yet, and you'd rather write it than wait for it.
What you bring
- 10+ years in digital marketing, including leading teams and owning multi-channel budgets, ideally in B2B SaaS, fintech, or another complex, high-consideration sale.
- A track record of building AEO/GEO programs, with examples of how you measured and moved AI-assistant visibility.
- Deep paid media expertise across Google, LinkedIn, and programmatic, plus hands-on experimentation with AI-native ad formats.
- Experience building agentic or AI-driven lifecycle and personalization programs on a modern stack (marketing automation, CDP or intent data, enrichment, orchestration).
- Experience shipping a personalized or adaptive web experience and running a disciplined testing program on it.
- Hands-on fluency with AI tooling: LLMs, agent frameworks, workflow automation, and AEO and enrichment platforms. You can build a working prototype yourself.
- Strong analytical and attribution chops, with the credibility to present pipeline impact to a CMO and executive team.
- Sound judgment on brand, accuracy, compliance, and data privacy for AI-generated content and automated decisions, especially with regulated buyers.
- A leader who develops people and can bring a team through a real change in how work gets done.
Bonus points
- Experience marketing to banks, fintechs, or other regulated industries.
- Experience with tools in our orbit, such as Salesforce, Marketo, 6sense, Clay, Apollo, AirOps, Ahrefs, or Mixpanel.
- A public body of work: writing, talks, or open-sourced workflows on AEO or agentic marketing.
Why Socure
Socure is the AI-native leader in identity verification and fraud prevention, trusted by thousands of organizations, including the largest banks and fintechs. We're scaling fast, the market is moving our way, and marketing has a real seat in how we grow. You'll have executive air cover, a team ready to move, and room to build something other companies will copy.
Socure is an equal opportunity employer that values diversity in all its forms within our company. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
If you need an accommodation during any stage of the application or hiring process—including interview or onboarding support—please reach out to your Socure recruiting partner directly.
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Compensation Range: $230K - $260K
Salary Context
This $230K-$260K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Socure, 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 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($245K) sits 16% above the category median. Disclosed range: $230K to $260K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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: $272,275; VP: $235,000.
Socure AI Hiring
Socure has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span New York, NY, US, New York, NY. Compensation range: $260K - $260K.
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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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