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About This Role
About AegisAI
We’re a team of ex-Google engineers and product leaders who built some of the largest defensive platforms on the planet—Safe Browsing and reCAPTCHA. Now, we’re striking out on our own to tackle an even bigger challenge: stopping the new wave of adversarial AI threats already hitting organizations today.
Backed by Battery Ventures, Accel, and Foundation Capital, we’re going after a $7B+ market ripe for disruption. Traditional rules-based security tools and bloated enterprise software don't stand a chance against AI-driven social engineering. We’re building native, agentic AI systems that think and adapt like human security analysts.
The Role
We are looking for an AI-Native Visual Designer to define and elevate the visual identity of AegisAI.
This is not a traditional design role where you wait weeks for briefs, push pixels manually, or pass off static Figma mockups. You will be expected to operate as an AI-native creator—leveraging cutting-edge AI tools (Midjourney, Runway, Stable Diffusion, ComfyUI, ChatGPT/Claude, generative code, and beyond) to multiply your speed, experiment ruthlessly, and turn complex AI and security concepts into sharp, compelling, category-defining visual assets.
You’ll work directly alongside our founders, Head of Product, and GTM teams to craft everything from product launch visual assets, dark-mode technical brand systems, enterprise web experiences, interactive threat reports, and high-stakes deck designs.
What You’ll Do
- Own the AegisAI Visual Identity: Scale and evolve our brand identity into a bold, high-precision aesthetic that reflects our position as the category leader in agentic AI security.
- Leverage AI-Native Design Workflows: Integrate generative visual models, AI vector generation, and automated design workflows directly into your daily stack to go from idea to polished execution in hours, not weeks.
- Translate Technical Complexity into Visual Clarity: Synthesize dense security topics—like multi-agent orchestration, threat graph analysis, and zero-day payload evasion—into intuitive, stunning visual metaphors, diagrams, and motion graphics.
- Build Web & Product Collateral: Design high-converting marketing pages, interactive landing pages, campaign graphics, and executive deck narratives that convert Fortune 500 CISOs and security leaders.
- Partner Across the Arena: Work side-by-side with GTM, Product, and Engineering in real-time fast feedback loops. No red tape, no endless feedback committees—just execution.
What We’re Looking For
- AI-Native Mindset: You don't view generative AI tools as a threat or a gimmick—they are integrated into your primary design stack. You know how to prompt, fine-tune, iterate, and polish AI-generated media to produce production-grade, pixel-perfect visuals.
- 3–6+ Years of Design Experience: A strong background in brand identity, visual design, communication design, or graphic design, ideally at a high-growth tech startup, top agency, or category-defining tech company.
- Exceptional Aesthetic Bar: A portfolio demonstrating mastery over typography, layout, color theory, data visualization, and motion design. You build work that looks premium, sharp, and futuristic, yet grounded and trustworthy.
- Technical & Domain Curiosity: You don't need to be a security researcher, but you live to understand complex systems. You are excited to learn how LLM reasoning, threat vectors, and multi-agent systems work so you can visually explain them.
- High Agency & Speed: You think in days and hours, not quarters and months. You thrive in ambiguous, fast-paced environments where you own your decisions and ship continuously.
Note to Candidates (Portfolio Requirement)
Please include a link to your portfolio or work samples with your application.
We care deeply about craftsmanship, taste, and speed. Your portfolio should showcase finished visual work as well as breakdown examples of your design process—specifically how you integrate AI tools (generative images, motion, workflows, or vector generation) alongside traditional design software to achieve polished, production-grade results.
Why This Is Hard (a.k.a. Why It’s Fun)
- The field evolves daily. Generative design tools and security threats change every week. What worked last month will be obsolete tomorrow.
- Translating "Invisibles" into "Visuals." AI agents and email threat vectors exist in background infrastructure. Making abstract, high-level AI concepts visually striking and intuitive is a unique design challenge.
- There’s no playbook. We are creating a new visual language for agentic AI defense. You’ll be in the arena shaping a global brand from day one.
Why We’ll Win
- We’ve done this before. Our team built core defense tech protecting billions of users (Safe Browsing, reCAPTCHA). This is our third time building a category-defining security company.
- AI and design are in our DNA. We don't bolt AI on—it’s built into our product and our internal operations from day one.
- Top 1% Team. We only hire top performers who are hungry to build at the bleeding edge.
- Flat, flexible, and fast. No politics, no bureaucracy. Just high-output people solving hard problems together.
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 Aegis AI, 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.
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.
Aegis AI AI Hiring
Aegis AI has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US.
Location Context
AI roles in Austin pay a median of $207,800 across 271 tracked positions. That's 3% below 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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