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About This Role
Who We Are
Eliza is a technology services company and Advanced-tier OpenAI partner that's dedicated to helping organizations build and deploy cutting-edge AI solutions. From generative AI and custom LLM integrations to predictive analytics and intelligent automation, we work across industries to bring real-world AI applications to life. Our projects combine deep technical expertise with hands-on client collaboration to solve high-impact problems.
The Shape of the Role
Our VP Cyber is a practice leader first. You'll build and run Eliza's Office of the CISO, the security practice we're launching to help enterprise security leaders get ahead of AI-driven threats. You'll own its revenue number, its client relationships, and the team that delivers the work. You'll report directly to the CTO and sit on Eliza's executive team.
Here's why now: Software has never been easier to build, and it's never shipped with less security. AI-assisted development is growing the amount of code every security team has to defend, and the same tools are making attackers faster. Security leaders feel that pressure today, and our partnership with OpenAI puts us in the room with them. Right now our technical leadership carries this work on top of their existing jobs. That got us early conversations with serious buyers. It won't get us a practice.
This is the first hire in the function which means you will build the practice team, playbook, and book of business. You'll also own Eliza's internal security program. It's lean today, but it gets interesting fast: we build composable, adaptive AI software, and securing it is a problem nobody has fully solved yet.
Two things to know before applying. First, we're an AI-native firm and we work that way internally: this practice is scoped to start lean because we expect you to use AI tools aggressively across everything you do, from threat research and code review to proposals, client education, and delivery playbooks. If AI isn't already core to how you work, this role will feel impossible. Second, this is a fast-paced, high-ownership environment. You'll get outcomes, not instructions. Priorities shift quickly, ambiguity is constant, and the bar is high.
Key Responsibilities
1. Build and own the Office of the CISO practice
- Own the practice P&L and revenue number starting from zero, including pricing, packaging, and which offers we lead with
- Define our service lines across security assessments, red, blue, and purple team work, and security training, and decide where AI-assisted delivery gives clients a real edge
- Set the practice strategy with the CEO and hold it accountable to pipeline and revenue targets
2. Lead with education and win CISO relationships
- Act as the face of the practice with CISOs and security leaders. Open doors, run executive briefings, and close engagements alongside our commercial team.
- Build the thought leadership that makes Eliza the first call security leaders make: talks, briefings, workshops, and a clear point of view on AI and security
- Sell by teaching. The CISOs we meet don't need a pitch. They need someone who has lived the problem and can explain what's changed.
3. Own the OpenAI security partnership
- Act as Eliza's senior security counterpart to OpenAI's security and partner teams, and hold your own technically with their leadership
- Turn partner leads and co-sell opportunities into scoped, funded engagements
- Meet and maintain the capability standards the partnership requires, starting with running security work on our own platform first
4. Build the delivery organization
- Hire and lead the practice team of security engineers and consultants, starting lean and scaling with demand
- Build repeatable delivery: workshop formats, assessment methods, evidence standards, and the quality bar every engagement is held to
- Stay hands-on. Early on, expect to be in the code, the findings, and the client room.
5. Serve as Eliza's Security Leader
- Own Eliza's internal security program, policies, and posture as we grow
- Define how we secure composable, adaptive AI software, a new threat surface without an established playbook
- Treat Eliza as customer zero. What we learn securing our own platform becomes what we sell.
To be successful, we expect someone to have…
- Current or recent experience operating as a VP or equivalent security executive. You're already doing this job at a high level.
- Experience building a services business, security consultancy, or managed or delivery services organization, including owning revenue
- Credibility with CISOs as a peer. You can walk into a room with a Fortune 500 security leader and they lean in.
- Deep technical roots. You still read code, understand how modern applications and cloud infrastructure break, and can pressure-test a finding yourself.
- A point of view on how AI changes attack and defense, backed by daily use. Show us how you use AI tools today to research threats, review code, and get more security work done, faster, at higher quality.
- A track record of thought leadership: speaking, writing, advising, or teaching that has shaped how other security leaders think
- A habit of owning outcomes, not tasks. You're comfortable building from zero with a revenue target and no playbook.
- Willingness to travel frequently for client and partner meetings
And maybe…
- Experience in high-stakes or highly regulated environments like financial services, national security, defense, or critical infrastructure
- A background in offensive security, red teaming, or security research
- Experience at a security services or penetration testing firm, or building a security practice inside a consulting firm
- Firsthand experience leading an organization through a major security incident, and the lessons that came with it
What We Offer
- Competitive executive compensation: base salary, meaningful equity, and upside tied to the growth of the practice you build
- A seat on Eliza's executive team
- Direct partnership with OpenAI's security organization on one of their top priority areas
- Remote flexibility, with in-person time where it matters
- The chance to define what AI-native security services look like for a whole market
- Full ownership from day one. You're building the practice, not inheriting one.
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 Eliza, 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.
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.
Eliza AI Hiring
Eliza has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
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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