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About This Role
Join Our Mission: To Save the World from Unsafe Mobile Apps! NowSecure is the mobile app security software company trusted by the world’s most demanding organizations and most advanced security teams.
As the standards\-based mobile app security and privacy company, NowSecure protects the Mobile App Economy. The world’s most demanding organizations, innovative mobile developers and advanced risk managers and compliance teams entrust NowSecure to safeguard millions of mobile app users across banking, insurance, high tech, IoT, retail, hospitality, energy and government sectors. Only NowSecure delivers the full solution suite of continuous security and compliance assessment with the depth, speed, accuracy, and efficiency to meet modern business demands. Dedicated to the open\-source community and standards including OWASP, ioXt and NIAP, NowSecure is SOC 2 certified and recognized by IDC, Deloitte, Gartner and TAG Cyber.www.nowsecure.com
Your Opportunity:
We're looking for a Senior Agentic Security Automation Engineer who thrives on technical challenges, enjoys building things from scratch, and has an insatiable curiosity for how software works, how it breaks, and how it can be analyzed autonomously.
In this role, your goal won't be to perform security assessments by hand. You'll design agentic workflows, build automation frameworks, integrate security tooling (including that built by our world renowned research team; the people behind Frida and radare2\), develop evaluation systems, and create the infrastructure that enables autonomous analysis of applications of all kinds, be it computer, phone or IoT.
Success in this role means helping transform security testing from a human\-limited activity into a technology\-enabled capability that can analyze more applications, execute more test coverage, identify more meaningful security issues, and continuously improve over time. Your first mission is to make our offensive security team faster and more effective in a repeatable way. Where it goes from there is genuinely open, and that's a big part of the appeal. You'll have a hand in shaping it.
What You’ll be Doing:
### Build Agentic Security Testing Systems
- Architect, build, and deploy advanced AI agents capable of autonomous reasoning, decision\-making, and security analysis.
- Design and implement multi\-step agentic workflows that replicate and scale expert security testing methodologies.
- Develop systems capable of analyzing applications on desktop software, IoT devices, APIs, and emerging technology platforms, building off the expertise of our best\-of\-breed mobile security testing.
- Create autonomous and human\-in\-the\-loop workflows that balance scale, accuracy, and trust.
### Design Security Automation Frameworks
- Develop reusable tools, skills, prompts, workflows, MCP servers, and agent orchestration infrastructure.
- Integrate static and dynamic analysis, reverse engineering and decompilation, network analysis, vulnerability intelligence, and custom security tooling into agent workflows, including the kind of instrumentation that powers Frida\-style runtime analysis.
- Design systems that allow security expertise to be reused and continuously improved over time.
### Research \& Innovation
- Stay current on advancements in agentic AI, offensive security, software assurance, and autonomous systems.
- Prototype and evaluate new approaches for increasing the scale and depth of security testing.
- Collaborate with security analysts and researchers to convert offensive security methodologies into scalable automation.
Who You Are:
If you're the kind of person who spends a weekend wiring together a new LangGraph workflow, builds a custom tool because the existing one doesn't quite fit, or finds yourself wondering whether an AI agent could perform a security task faster, better, or at a scale impossible for human teams alone, you'll fit right in.
This is a highly autonomous role. We're looking for someone who can identify opportunities, define milestones, conduct research, and drive projects from concept to production with minimal oversight. You'll have significant freedom to experiment, iterate, and help define the future of AI\-driven security testing.
Skills and Experience Needed for Success:
- Coding experience in Python, JavaScript, or TypeScript. You don't need to be an expert. You do need to write clean, production\-quality code and reliably pick up what you don't already know.
- A security background. An offensive security background (penetration testing, application security, security consulting, or research) is ideal, but a solid security background of any kind will serve you well here.
- Curiosity and aptitude for AI. Hands\-on experience building with LLMs (Claude, GPT, Gemini, or similar), agentic frameworks, or Retrieval\-Augmented Generation (RAG) is a real advantage, but the ability and drive to learn it fast matters just as much. We'd rather hire a sharp, motivated builder who's newer to AI than someone who's done with learning.
- U.S. citizenship. This role supports U.S. government work and requires U.S. citizenship.
Bonus Points if You Have Any of the Below:
- Demonstrable experience building agentic workflows with LangGraph, Semantic Kernel, AutoGen, the OpenAI Agents SDK, or similar, including multi\-agent systems and autonomous execution pipelines.
- Experience integrating telemetry from multiple security tools (e.g., Zscaler, iVerify, Omnissa) into a unified analysis engine or detection pipeline, and correlating those signals to proactively surface attempted or successful attacks.
- Production experience with tool/function calling, structured outputs, prompt engineering, context management, and evaluation frameworks.
- Experience with Java, Go, or additional languages.
- Production engineering and MLOps: scalable APIs and services, Docker/Kubernetes, CI/CD, observability, and one of AWS, Azure, or GCP.
- Mobile or desktop application security testing experience.
- Reverse engineering experience.
- Experience building MCP servers.
- Experience training or fine\-tuning machine learning models.
- A track record of building custom security tools.
- Contributions to open\-source security or AI projects.
- Experience supporting both commercial and U.S. government customers (a real plus).
- Published security research, presentations, or conference talks.
We Value Diversity
We believe that the best ideas come from teams where diverse points of view uncover new solutions to hard problems. We welcome and value team members who bring diverse life experiences, educational backgrounds, cultures, and work experiences.
Compensation \& Benefits
NowSecure is committed to fair and equitable compensation practices. Placement within the pay range is dependent on a variety of factors including, but not limited to, relevant work experience, skills, certifications, job level, supervisory status, and location. The base salary range for this position for all U.S. candidates is $120,000 \- $160,000 per year, with eligibility for bonuses, equity grants and a comprehensive benefits package that includes health insurance, 401k with company match, paid parental leave, Home Office Stipend, and flexible PTO. In addition to working in a remote\-first work environment.
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Salary Context
This $120K-$160K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At NowSecure, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($140K) sits 36% below the category median. Disclosed range: $120K to $160K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
NowSecure AI Hiring
NowSecure has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $160K - $160K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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