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About This Role
Job Description:
Amazing Career Moments Happen Here
Transforming the insurance industry is ambitious, we know. That’s why at Applied, we’re building a team that shows up every day ready to learn, willing to try new things, and driven to deliver innovative software and services that make us indispensable to our customers – all within a culture built on values that make us indispensable to each other too. With 40\+ years of experience in the insurtech game, we’re not just redefining what’s achievable, we’re creating a place where amazing career moments are made possible. Position Overview
Applied Systems is building out its AI security program and seeking an experienced security engineer to help secure our Large Language Models, generative AI systems, and ML infrastructure. This is an emerging role for an engineer who understands traditional security principles deeply and is eager to apply them to the rapidly evolving world of AI, from prompt injection and model poisoning to data privacy in training pipelines. You won't need to be an ML researcher or data scientist, but you should have foundational knowledge of how machine learning works and be genuinely curious about AI\-specific security threats. This role offers the opportunity to shape how Applied approaches AI security, work with cutting\-edge technology, and grow expertise in an area where few security engineers have deep experience. Ideal for someone who values both hands\-on security work and the challenge of learning an emerging domain.
What You’ll Do* Evaluate and assess the security posture of Large Language Models (LLMs) and generative AI systems used within or by Applied Systems
- Conduct threat modeling and security architecture reviews for AI/ML systems and their data pipelines
- Implement and maintain security controls for AI model training, fine\-tuning, and inference infrastructure
- Develop and maintain security baselines and hardening configurations for AI platforms and frameworks
- Identify and help remediate security vulnerabilities specific to AI/ML systems (prompt injection, model poisoning, data exfiltration, adversarial attacks)
- Implement data security and privacy controls for AI training datasets and inference inputs
- Develop and maintain security runbooks for AI systems incident response
- Participate in security reviews of AI/ML applications and vendor AI services
- Contribute to the development of internal AI security policies and guidelines
- Assist with proof\-of\-concept builds for AI security solutions and controls
- Stay current with emerging AI security threats, research, and industry best practices
- Contribute to internal security training related to AI risks and secure AI development
We’re looking for someone who:* Has the ability to work from an Applied Systems office or 100% remotely
- Your experience should include some or all of the following:
+ Minimum of 3\-5 years' experience in security engineering or DevSecOps roles
+ Demonstrated foundational understanding of machine learning concepts, ML workflows, and common frameworks (PyTorch, TensorFlow, scikit\-learn)
+ Working knowledge of Large Language Models, transformer architectures, and generative AI applications
+ Experience with or strong understanding of LLM security concerns (prompt injection, jailbreaking, data poisoning, model extraction)
+ Experience with container security, Kubernetes security, and securing AI workloads in containers
+ Knowledge of cloud security in AI contexts (GPU security, distributed training security, data protection in ML pipelines)
+ Understanding of secure software development practices and supply chain security as applied to ML models
+ Knowledge of model governance, versioning, and secure model deployment
+ Experience with infrastructure\-as\-code technologies (Terraform, Ansible)
+ Experience with one or more scripting languages (Python, Bash, Go)
+ Understanding of encryption, key management, and data privacy (especially PII in training data)
+ Familiarity with vulnerability scanning and secure code practices
+ Working knowledge of compliance frameworks and their application to AI systems (GDPR, SOC 2, etc.)
+ Experience with securing data pipelines and ETL processes
+ Excellent written and verbal communication skills
+ Demonstrated ability to work independently and as part of a team
+ Willingness to rapidly learn new AI technologies and security frameworks
- You may also have:
+ Certification in security (Security\+, CISSP, etc.)
+ Experience with specific LLM platforms (OpenAI API, Anthropic Claude, Google Vertex AI, Azure OpenAI)
+ Experience with ML security tools and platforms (Robust Intelligence, Arthur AI, etc.)
+ Participation in AI security research, publications, or conferences
+ Experience in threat modeling for AI systems
+ Background in security research or penetration testing
+ Experience with red\-teaming AI systems
+ We know that talent comes from all backgrounds and experience levels. We encourage military members and their spouses as well as candidates without a degree or a background in tech to apply!
Location
Candidate will need to reside in North America, working arrangement will be remote. When You Join Team Applied, You Can Expect: A culture that values who you are and recognizes that you aren’t just an employee; you are a teammate, and you matter. We thrive on the benefits of our different experiences and celebrate the uniqueness our teammates bring to work with them every day. We flex our time together, collaborating remotely and in\-person to empower our teams to work in the ways that work best for them. A comprehensive benefits and compensation package that centers our teammates and helps them to bring their best to work every day:
Medical, Dental, and Vision Coverage
Holiday and Vacation Time
Health \& Wellness Days
A Bonus Day for Your Birthday
Learn more about the people behind our products at https://www1\.appliedsystems.com/en\-us/about\-us/jobs/
Our targeted starting base salary in the United States for this position ranges from $80,000 \- $120,000\. To determine a new team member’s starting pay, we consider a variety of factors, including someone’s depth, breadth, and variety of experience, skills, and responsibilities. Depending on the role, team members may also be eligible to participate in additional compensation plans such as bonus and commission. Your Security Matters:
Our candidates’ personal information and online safety are top of mind for us. At Applied, we proactively protect your personal information and only communicate with candidates via a secure @appliedsystems.com email or through our official careers portal. Recruiters will never request payments, ask for financial account information or sensitive information like social security numbers. EEO Statement*Applied Systems is proud to be an Equal Employment Opportunity Employer. Diversity and Inclusion is a business imperative and is a part of building our brand and reputation. At Applied, we don’t discriminate, and we are committed to recruit, develop, retain, and promote regardless of race, religion, color, national origin, sexual orientation, gender identity, disability, age, veteran status, and other protected status as required by applicable law.*
\#LI\-Remote
Salary Context
This $80K-$120K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Applied Systems, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($100K) sits 53% below the category median. Disclosed range: $80K to $120K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Applied Systems AI Hiring
Applied Systems has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $120K - $120K.
Location Context
AI roles in Austin pay a median of $214,343 across 143 tracked positions.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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