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About This Role
BeyondTrust is a place where you can bring your purpose to life through the work that you do, creating a safer world through our cybersecurity SaaS portfolio.
Our culture of flexibility, trust, and continual learning means you will be recognized for your growth, and for the impact you make on our success. You will be surrounded by people who challenge, support, and inspire you to be the best version of yourself.
The Role
We are looking for a Senior Software Engineer specializing in AI and Security to help design and build the next generation of intelligent security systems. In this role, you will develop platforms and capabilities that use machine learning, behavioral analytics, and automation to detect threats, reduce risk, and strengthen the security posture of the organization.
You will work at the intersection of security engineering, machine learning, and large\-scale systems, helping transform security data into actionable insights and automated defenses. The ideal candidate enjoys solving complex problems, working with large datasets, and building production\-grade systems that protect critical infrastructure.
This role partners closely with security engineering, data science, product engineering, and cloud platform teams to deliver AI\-driven capabilities that improve detection, investigation, and response across the enterprise. This role is expected to actively leverage AI\-assisted development as a core part of how software is designed, built, and delivered.
What You'll Do
Build AI\-Powered Security Systems
- Use tools such as GitHub Copilot and AI coding assistants to accelerate the development of secure applications and internal platforms.
- Develop practical prompts, workflows, and guardrails that enable engineers with security expertise to use AI as an active development partner in building functional, secure software.
- Apply AI\-assisted development to accelerate coding, testing, documentation, refactoring, and threat\-aware design reviews.
Deliver Applications to Production
- Take applications from concept to production by applying strong engineering principles across architecture, deployment, observability, and reliability.
- Build and operate CI/CD pipelines, containerized deployments, infrastructure as code, and secure release processes.
- Ensure systems are production\-ready through monitoring, testing, operational readiness, and security\-by\-design practices.
- Apply clear engineering and security principles, including least privilege, defense in depth, and scalable system design.
Build Security Data Pipelines
- Design and implement data ingestion pipelines that collect and normalize security telemetry from multiple sources.
- Work with distributed data platforms and large\-scale datasets to support analytics, detection, and AI\-driven models.
- Ensure secure and compliant handling of sensitive data throughout the pipeline lifecycle.
Improve Detection \& Response
- Integrate AI\-driven insights into security platforms such as SIEM, identity systems, and cloud security tools.
- Build automation that assists analysts with alert triage, investigation, and remediation.
- Help evolve security operations from reactive monitoring to proactive, intelligence\-driven defense.
Collaborate Across Engineering and Security
- Partner with security teams, engineers, and data scientists to identify high\-impact detection and automation opportunities.
- Enable teams to embed security intelligence directly into products and infrastructure.
- Contribute to architectural decisions that improve security posture at scale.
Research Emerging AI \& Security Techniques
- Stay current on emerging threat techniques, adversarial AI, and machine learning applications in security.
- Prototype and evaluate new detection approaches, models, and methodologies.
- Contribute to innovation in AI\-assisted security engineering and defense.
What You'll Bring
Education and Experience
- 7\+ years of experience in software engineering or security engineering
- Experience building production systems using machine learning or large\-scale analytics
- Background working with security telemetry, threat detection, or behavioral analytics is strongly preferred
Software Engineering
- Experience using AI\-assisted development tools (GitHub Copilot, Claude, or similar AI\-assisted development tools) in real\-world software delivery
- Strong programming skills in Python, Go, Java, Rust, or similar
- Experience designing distributed systems and microservices
- Experience building high\-scale data processing systems
Security Engineering
- Familiarity with modern attack techniques and security detection methods
- Experience with security platforms such as SIEM, EDR, cloud security platforms, or identity systems
Cloud \& Infrastructure
- Experience building systems in AWS, Azure, or GCP
- Familiarity with containers, Kubernetes, and CI/CD pipelines
Data Engineering
- Experience working with large\-scale datasets and distributed data processing
- Familiarity with streaming or batch processing technologies
Certifications Preferred
- CISSP, OSCP, Security\+, or other security certifications
- Cloud certifications (AWS, Azure, GCP)
- Machine learning coursework or certifications
Nice To Have
- Machine learning
- Knowledge of identity security, behavioral analytics, or threat intelligence
- Experience with security automation or SOAR workflows
- Contributions to open\-source security or machine learning projects
Better Together
Diversity. Inclusion. They're more than just words for us. They are the guiding values of how we build our teams, cultivate leaders, and create a culture where people feel connected.
We take care of our employees so they can take care of our customers. Customers who come from all walks of life just like us. We hire incredible people from diverse backgrounds because when we are different together, we are stronger together.
About Us
BeyondTrust is the global identity security leader protecting Paths to Privilege™. Our identity\-centric approach goes beyond securing privileges and access, empowering organizations with the most effective solution to manage the entire identity attack surface and neutralize threats, whether from external attacks or insiders.
BeyondTrust is leading the charge in transforming identity security to prevent breaches and limit the blast radius of attacks, while creating a superior customer experience and operational efficiencies. We are trusted by 20,000 customers, including 75 of the Fortune 100, and our global ecosystem of partners.
Learn more at www.beyondtrust.com.
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Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 26,159 AI roles we're tracking, AI Software Engineer positions make up 2% of the market. At BeyondTrust, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $235,100 based on 665 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
BeyondTrust AI Hiring
BeyondTrust has 1 open AI role right now. They're hiring across AI Software Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $156,000 across 1,221 positions. About 7% of all AI roles offer remote work.
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
AI Hiring Overview
The AI job market has 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 roles).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
The AI Job Market Today
The AI job market spans 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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.
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