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About This Role
About the Role NinjaOne’s Application Security Engineering team is looking for a Senior Software Engineer to assist in building an AI platform that discovers vulnerabilities in production code, validates findings against real attack paths, and proposes tested fixes. You will build and own the software that makes this work reliable at scale, partnering with Security Architecture and Product Engineering to turn security research into actionable improvements across our cloud platform and endpoint agents.
This is a hands-on software engineering role with ownership from design through production. You will solve problems in agent orchestration, durable execution, context management, evaluation, and cost control. Your work will help engineers find and fix meaningful vulnerabilities earlier, with evidence they can trust.
We are hiring four engineers across two focus areas: Vulnerability Discovery and Finding Validation, with two openings in each. We will discuss your preference during interviews. Discovery develops and tests vulnerability hypotheses; Validation independently challenges findings and develops evidence-backed fixes. Separate model families and independent ownership help each stage check the other’s conclusions. Location - We are flexible on remote working from home, if you are located in the USA and reside in one of the following states - CA, CO, CT, FL, GA, IL, KS, MA,MD, ME,NJ, NC, NY,OH, OR, TN, TX, VA, and WA. We have physical offices in Austin, TX and Tampa, FL, if you prefer a hybrid option. Onsite interviews may be required for this role.
We hire the best software engineers, but experience in our stack can’t hurt: NinjaOne is built on Java, Kotlin, C++, Golang and Postgres; supporting millions of user endpoints and running as a scalable cloud service in AWS. Knowing large-scale datastore bottlenecks, asynchronous application design and client-server architecture will help you. What You’ll be Doing* Build and operate a production AI vulnerability platform with resumable workflows, bounded agent execution, model routing, and per-repository budgets across AWS Bedrock and OpenAI-compatible APIs.
- Engineer repository context, threat models, and coverage tracking so agents can reason about entry points, trust boundaries, tenant isolation, and relevant code paths.
- Build independent validation, stable finding identity, and deduplication. Ground reachability and exploitability of judgments in code, deployment evidence, and reproducible tests.
- Develop patch and regression-test workflows that demonstrate a failing test before a fix and a passing test afterward, check for regressions, and require human review before merging.
- Protect agent execution with sandboxing, least-privilege tool access, and controls for untrusted repository content, prompt injection, secrets, and sensitive data.
- Partner with security and engineering teams to deliver findings through SARIF and existing workflows. Measure validated finding quality, coverage, reliability, and cost per validated finding; communicate results and share engineering practices.
- Other duties as needed
About You
- 5+ years of software engineering experience, including ownership of production services or automation platforms, and hands-on experience building LLM-backed applications or agent workflows.
- Strong Python skills, including asynchronous concurrency, typing, testing, packaging, and profiling. You can design, ship, debug, and maintain production-quality software.
- Experience building multi-step workflows with durable state, idempotent retries, task queues, termination conditions, observability, and resource limits. You can explain the failure modes and tradeoffs of systems you have shipped.
- Hands-on experience with LLM APIs, tool calling, structured outputs, context management, and evaluation. You can assess when to use an agent framework and when simpler orchestration is more reliable.
- Experience with AWS or comparable cloud infrastructure and distributed systems. Familiarity with model provider integrations, container isolation, and secure handling of code and credentials.
- Working knowledge of application security, including authentication, authorization, common vulnerability classes, and trust boundaries. You can use evidence to distinguish a plausible hypothesis from an actionable finding.
- Ability to work through ambiguity, collaborate with product and security engineers, and communicate technical decisions and results to engineering and leadership audiences.
- Bachelor’s degree in Computer Science, Information Technology, or a related field, or equivalent practical experience.
Focus area experienceYou do not need expertise in both areas. We are interested in depth aligned with the work you want to own.
- Vulnerability Discovery: static analysis, syntax trees and call graphs, code retrieval, threat modeling, or sandboxed proof-of-concept execution. Experience reading C or C++ and understanding native-code vulnerabilities is valuable for our endpoint agents.
- Finding Validation: security triage, clustering or near-duplicate detection, benchmark design, ground-truth labeling, or automated patching and test infrastructure. You understand how to measure precision and the limits of recall estimates without a labeled dataset.
Additional experience that would be useful* SARIF, SAST/SCA integrations, durable execution frameworks, or tracing and evaluation tools for LLM applications.
- Securing agentic systems through authorization, sandboxing, tool permissions, or defenses against malicious inputs.
- Open-source contributions, security research, program repair, or other examples of software you have built and operated.
About Us NinjaOne unifies IT to simplify work for nearly 40,000 customers in 140+ countries. The NinjaOne Unified IT Operations Platform delivers endpoint management, autonomous patching, backup, and remote access in a single console to improve efficiency, increase resilience, and reduce spend. By automating IT and managing all endpoints, organizations give employees a great technology experience at work. NinjaOne is obsessed with customer success and has retained a 98% customer satisfaction score for more than 5 years. What You’ll Love We are a collaborative, kind, and curious community. We honor your flexibility needs with full-time work that is hybrid remote. We have you covered with our comprehensive benefits package, which includes medical, dental, and vision insurance. We help you prepare for your financial future with our 401(k) plan. We prioritize your work-life balance with our unlimited PTO. We reward your work with opportunity for growth and advancement. Additional Information This position is NOT eligible for Visa sponsorship.
- Due to operational policies, NinjaOne is unable to hire for this role within the city limits of Chicago. We will consider all qualified candidates who reside outside of the city proper or are willing to self-relocate.
Starting pay for the successful applicant depends on a variety of job-related factors, including but not limited to location, market demands, experience, job-related knowledge, and skills. The benefits available for this position include medical, dental, vision, 401(k) plan, life insurance coverage and PTO. For roles based in California, Colorado, Maryland, New Jersey, or Washington the base salary hiring range for this position is $170,000 - $230,000 per year.
For roles based in New York, the base salary hiring range for this position is $170,000 - $230,000 per year.
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, genetic information, marital status, veteran status, or any other status protected by applicable law. We are committed to providing an inclusive and diverse work environment.
#LI-MM1 #LI-Remote #LI-Hybrid #BI-Remote #BI-Hybrid
Salary Context
This $170K-$230K range is above the median for AI Software Engineer roles in our dataset (median: $192K across 146 roles with salary data).
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 8,943 AI roles we're tracking, AI Software Engineer positions make up 4% of the market. At NinjaOne, 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 $220,000 based on 1,448 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($200K) sits 9% below the category median. Disclosed range: $170K to $230K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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: $272,250; VP: $235,000.
NinjaOne AI Hiring
NinjaOne has 2 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer. Positions span Remote, US, Ohio, United States. Compensation range: $230K - $230K.
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 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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).
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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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