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Overview:
About the Role \& Team
IDC is seeking a Research Vice President, AI Security and Trust to serve as a senior market\-facing leader in one of the most dynamic areas of the security market. This role will shape IDC’s global point of view on how organizations secure AI models, data, agents, applications, and infrastructure.
The Research Vice President will define and lead a forward\-looking research agenda spanning AI runtime security, AI trust and governance, AI data security, agentic identity management, AI\-enabled security operations, and AI factory / AI infrastructure security. The role requires strong market vision, executive presence, analytical depth, and commercial leadership.
This individual will engage regularly with senior vendor executives, enterprise buyers, investors, media, and industry stakeholders to assess market requirements, technology adoption patterns, investment priorities, and competitive dynamics. The successful candidate will help establish IDC as a leading voice in AI security and trust, while supporting syndicated research growth, custom research opportunities, and strategic client engagement. What You’ll Do
- Lead IDC’s research agenda for the emerging AI security and trust market, including high\-growth categories such as AI runtime security, AI trust and governance, AI data security, agentic identity, AI\-enabled SOC, and AI infrastructure security.
- Shape IDC’s market taxonomy, research priorities, and point of view on how AI security is evolving across cybersecurity, AI, cloud, data, software, and digital infrastructure markets.
- Develop and publish differentiated research, including market analysis, vendor assessments, forecasts, buyer guidance, thought leadership, and executive\-level advisory content.
- Conduct and oversee market sizing, forecasting, competitive analysis, and trend analysis for emerging AI security segments.
- Track vendor strategies, startup activity, funding trends, partnerships, product innovation, regulatory developments, and emerging buyer requirements across the AI security landscape.
- Engage senior technology vendor executives through briefings, inquiries, advisory calls, strategy sessions, executive discussions, and industry events.
- Engage enterprise security, AI, cloud, infrastructure, and data leaders to understand adoption priorities, governance challenges, implementation patterns, and buying behavior.
- Represent IDC externally through presentations, webinars, conferences, media engagement, investor discussions, and executive client meetings.
- Partner with IDC analysts across security, AI, cloud, infrastructure, data, and software to build integrated, cross\-domain research perspectives.
- Mentor and collaborate with analysts and research colleagues to strengthen IDC’s collective coverage of AI security, trust, and governance.
- Support and help grow syndicated subscriptions, custom research, consulting opportunities, and strategic account engagement tied to IDC’s expanding coverage of AI security and trust.
Preferred Coverage Areas
- The role may include leadership across several of the following domains:
- AI Runtime Security: Protection against LLM attacks, prompt injection, misuse, model manipulation, and emerging runtime enforcement approaches.
- AI Trust and Governance: Governance frameworks, policy controls, compliance, assurance, model oversight, and responsible AI security practices.
- AI Data Security: Discovery, classification, protection, and governance of AI\-ready data, including key and secrets management and related cryptographic issues.
- Agentic Identity Management: Identity, authentication, authorization, and privilege control for nonhuman identities, agents, and autonomous systems.
- AI\-Enabled SOC / Agentic SOC: Use of AI in security operations, automation, analyst augmentation, and emerging vendor ecosystems in next\-generation SOC tooling.
- AI Infrastructure Security: Security requirements for AI factories, accelerated infrastructure, cloud and hybrid deployments, model hosting environments, and AI workload protection.
What You Bring
- Bachelor’s degree required; advanced degree preferred.
- 12–15\+ years of relevant experience in technology research, advisory, consulting, product strategy, cybersecurity, AI, cloud, or related markets.
- Deep understanding of cybersecurity markets, vendors, buyer priorities, and emerging technology adoption patterns.
- Strong knowledge of AI, cloud, data, software, and infrastructure trends, especially where they intersect with security, trust, governance, and risk.
- Demonstrated ability to define research agendas, analyze emerging markets, synthesize qualitative and quantitative inputs, and produce clear, differentiated market insight.
- Proven executive presence, including the ability to engage with CISO, CIO, CTO, product, strategy, investor, and senior vendor leadership audiences.
- Strong written and verbal communication skills, including the ability to present complex market shifts clearly to senior executive audiences.
- Experience engaging with technology vendors, enterprise buyers, investors, media, and industry influencers.
- Strong commercial instincts, including the ability to support syndicated research growth, client inquiry, custom research, consulting, and strategic account development.
- Ability to work independently while influencing and collaborating effectively across a global research organization.
- Strong organizational and project management skills, with the ability to balance recurring research deliverables, client engagement, thought leadership, and commercial support.
- Proficiency with Microsoft Office tools, especially PowerPoint and Excel; experience with forecasting, survey analysis, and market modeling is a plus.
- Willingness to travel for client meetings, industry events, and team collaboration.
Why This Role Stands Out
At IDC, your work helps shape how the world understands technology and where it goes next. You collaborate with curious, high\-caliber colleagues who value rigor, integrity, and shared success. As the premier global provider of trusted technology intelligence, IDC equips business and technology leaders with the evidence they need to make confident decisions. Our insights inform strategy, investment, and innovation across industries and regions.
Recognized by IIAR as Analyst Firm of the Year for five consecutive years, IDC sets the standard for credibility and impact. With more than 1,000 analysts worldwide and a truly global perspective, we combine deep expertise with practical relevance. Here, your ideas matter, your voice is heard, and your contributions provide the insights leaders rely on every day. It is meaningful work, backed by a culture that supports growth, collaboration, and long\-term career development with a globally respected brand. What We Offer* 15 vacation days (prorated based on start date)
- 12 company\-paid holidays
- 6 paid sick days (prorated based on start date; may vary by state)
- Medical, dental, and vision coverage
- 2 floating holidays (prorated based on start date)
- 1 volunteer day
- 401(k) company match (IDC matches 3% on the first 6% of employee contributions)
- Company\-paid short\-term disability
- Company\-paid life insurance
- Company\-paid parental leave
Compensation Transparency
At IDC, we are committed to fair and equitable pay practices. Employees are compensated equitably for their work, aligned with their skills and experience. Salary and incentive structures are determined through a rigorous process that considers experience, education, certifications, role\-specific requirements, internal equity, and verified U.S. market data from an independent third\-party partner.
The expected total annual compensation, depending on location and experience, is between $150,000 – $235,000, inclusive of base salary and variable compensation. Equal Opportunity Employer*IDC is committed to providing equal employment opportunities for all qualified persons. Employment eligibility verification required. We participate in E\-Verify.*
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\#LI\-Remote
Salary Context
This $150K-$235K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At IDC Research 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 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 $181,170 based on 12,692 positions with disclosed compensation. This role's midpoint ($192K) sits 6% above the category median. Disclosed range: $150K to $235K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
IDC Research Inc. AI Hiring
IDC Research Inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $235K - $235K.
Location Context
AI roles in Austin pay a median of $215,300 across 523 tracked positions. That's 8% 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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