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Title: Data Privacy, Cybersecurity & AI Compliance Manager
Location: Austin, TX / Dallas, TX / Morristown, NJ (Hybrid)
Reporting to: Sr. Enterprise Compliance Director
About Hippo:
Hippo was built on a promise: make homeownership effortless. Nearly a decade later, that mission still drives us. We use technology and data to help our customers stay ahead of problems and protect what matters most.
Today, that same tech-native approach powers our work beyond homeowners. Hippo operates as a diversified carrier platform, partnering with MGAs to deliver tailored program solutions that help them grow and deliver better customer experiences. Behind that work is a team that values ownership, curiosity, collaboration, and continuous improvement.
If you're energized by building what's next, we'd love to meet you.
About You:
You are a compliance professional who lives at the intersection of data privacy, cybersecurity, and AI governance. You're not necessarily an attorney, but you know how to translate complex regulatory requirements into practical, operational compliance programs, and you know when an issue needs to go to Legal for interpretation or sign-off. You've built or matured compliance programs against frameworks like the CCPA/CPRA and other state privacy laws, the TCPA and related marketing rules, the NYDFS Cybersecurity Regulation (23 NYCRR 500), the NAIC Insurance Data Security Model Law, and emerging AI regulations (e.g., Colorado AI Act, EU AI Act, NAIC AI model bulletins). You're as comfortable running day-to-day privacy operations, working a live security incident alongside InfoSec, or writing a quarterly report for a board committee as you are designing the program those activities live in. Insurance industry experience is a strong plus, since you understand the regulatory overlay carriers and producers face beyond standard corporate compliance. You're detail-oriented, comfortable managing multiple regulatory threads at once, and skilled at partnering across Legal, Security, Engineering, Marketing, and the business to keep Hippo ahead of a fast-moving regulatory landscape.
What You'll Do:
- Privacy Program Build & Ownership: Design, build, and run Hippo's enterprise data privacy compliance program, maturing it from today's operational footing into a documented, auditable program: governance and accountability structure, data inventory and records of processing, privacy impact assessment process, policy framework, program metrics, and a regulatory change management process covering CCPA/CPRA, other state privacy laws, and insurance-specific privacy requirements (e.g., GLBA and state insurance privacy laws)
- Privacy Operations & Consumer Rights: Own Hippo's privacy mailbox and consumer rights request process end to end: intake, verification, tracking, and timely response to access, deletion, correction, and opt-out requests, with documentation sufficient to withstand regulatory scrutiny
- Marketing & Advertising Compliance: Own compliance for marketing and outreach channels, including TCPA/telemarketing and SMS consent requirements (CTIA messaging standards, consent capture and logging, opt-out handling), CAN-SPAM, and website tracking technologies (cookies, pixels, session replay) and consent management; partner with Marketing and Growth teams on campaign and disclosure review
- Cybersecurity Regulatory Compliance: Support compliance with insurance-specific cybersecurity regulations (NYDFS 23 NYCRR 500, NAIC Insurance Data Security Model Law and state adoptions) and general frameworks (e.g., NIST); support annual certifications, risk assessments, and regulatory exams and audits, and maintain required documentation
- Cyber Event Handling: Partner with Information Security on the triage, investigation, containment, and resolution of cyber events; own the compliance workstream for each event, including documentation, materiality and notification analysis support (state breach statutes, NYDFS 72-hour reporting), and remediation tracking, escalating legal determinations to counsel
- AI Governance & Compliance: Build and maintain Hippo's AI compliance framework, including use case intake, risk assessment, inventory, and monitoring for AI/ML systems across the business; track state, federal, and international AI regulation relevant to insurance (algorithmic accountability, AI in underwriting, claims, and pricing, model governance requirements)
- Vendor & Third-Party Risk: Conduct privacy and data-protection reviews of vendors and third-party service providers, support the data processing agreement process with Legal, and partner with InfoSec on third-party security diligence, including NYDFS Section 500.11 TPSP requirements
- Policies, Standards & Training: Draft, maintain, and operationalize internal policies, standards, and procedures for data privacy, cybersecurity, and AI use, and develop and deliver company-wide training and awareness on those policies, in coordination with the Sr. Enterprise Compliance Director and Legal
- Regulatory Liaison Support: Serve as a day-to-day point of contact for regulatory inquiries, exams, and audits touching privacy, cyber, and AI, coordinating responses and escalating legal interpretation questions to attorney oversight
- Cross-Functional Partnership: Work closely with Legal, Information Security, Engineering, Marketing, and business units to embed privacy, security, and AI compliance requirements into products, processes, and vendor relationships
Must Haves:
- 5+ years of experience in data privacy, cybersecurity, and/or AI governance compliance within a regulated industry, including hands-on operational experience (consumer rights requests, incident response, marketing compliance), not just policy work
- Bachelor's degree; one or more relevant certifications strongly preferred: CIPP/US, CIPM, or CIPT (IAPP privacy certifications), AIGP (IAPP Artificial Intelligence Governance Professional), CISSP, CISM, CRISC, or CDPSE
- Demonstrated experience building or significantly maturing a privacy or compliance program, not solely running an established one
- Working knowledge of key privacy, marketing, and cybersecurity regulatory frameworks (CCPA/CPRA and other state privacy laws, TCPA, GLBA, NYDFS 23 NYCRR 500, NAIC Insurance Data Security Model Law)
- Familiarity with the emerging AI regulatory landscape and how it applies to insurance use cases (underwriting, claims, pricing, customer service)
- Experience supporting security incident response and breach notification analysis in partnership with information security and legal teams
- Strong written communication skills, including experience preparing reporting for senior management, board, or committee audiences
- Experienced working in close partnership with legal counsel and knows when to escalate for legal interpretation
- Strong program and project management skills; able to manage multiple regulatory workstreams simultaneously
- Technical aptitude and the ability to quickly learn new technologies and platforms (privacy management, consent management, and GRC tooling a plus)
Nice to Haves:
- Insurance industry experience strongly preferred (P&C a plus)
Benefits and Perks:
Hippo treats its team members with the same level of dedication and care as we do our customers, which is why we're fortunate to provide all of our Hippos with:
- Healthy Hippos Benefits - Multiple medical plans to choose from and 100% employer covered dental & vision plans for our team members and their families. We also offer a 401(k)-retirement plan, short & long-term disability, employer-paid life insurance, Flexible Spending Accounts (FSA) for health and dependent care, and an Employee Assistance Program (EAP)
- Equity -This position is eligible for equity compensation
- Training and Career Growth - Training and internal career growth opportunities
- Flexible Time Off - You know when and how you should recharge
- Little Hippos Program - We offer 12 weeks of parental leave for primary and secondary caregivers
- Hippo Habitat - Snacks and drinks available and catered lunches for onsite employees
The Morristown, NJ base pay range for this role is $120,000 - $179,500. Exact compensation may vary based on several job-related factors that are unique to each candidate, including but not limited to: skill set, experience, education/training, location, business needs and market demands.
Hippo is an equal opportunity employer, and we are committed to building a team culture that celebrates diversity and inclusion. Hippo's applicants are considered solely based on their qualifications, without regard to an applicant's disability or need for accommodation. Any Hippo applicant who requires reasonable accommodations during the application process should contact the Hippo's People Team to make the need for an accommodation known.
Applicant & Employee Privacy Notice (CCPA/CPRA)
Salary Context
This $120K-$179K 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 Hippo Insurance, 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 in Demand for This Role
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. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($149K) sits 29% below the category median. Disclosed range: $120K to $179K.
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.
Hippo Insurance AI Hiring
Hippo Insurance has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Morristown, NJ, US. Compensation range: $179K - $179K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 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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