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AI\-Driven Application Security Triage Engineer
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Location: Remote
Hours: 8\-hour workday with flexible start times
Pay Rate: $90/hr on W2
Type of Hire: 1\-year contract to start; potential extension based on performance
What a Day Looks Like
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You start your morning scanning the latest SCA, SAST, and DAST findings. Critical and high\-risk items are quickly validated, false positives are filtered out, and you document clear remediation guidance. When a PatchNow Critical event lands, you lead the scope analysis, route owners, and drive mitigation steps through to verified closure. If a threat intelligence escalation appears, you coordinate a fast, structured response.
By midday, you’re drafting concise briefs on newly disclosed and frontier\-model\-influenced vulnerabilities, translating technical details into action plans for development teams. In the afternoon, you’re hands\-on with AI\-enabled security tooling—testing frontier\-model capabilities for code reasoning, triage acceleration, and remediation recommendations while ensuring auditability, secure use controls, and human\-in\-the\-loop review.
Before you wrap, you reinforce our software supply chain security—from dependency hygiene (SBOM visibility, malicious package detection, policy enforcement) to safeguarding developer IDEs, plugins/extensions, code\-assist tools, package managers, and CI integrations against compromised components and unsafe configurations.
Core Responsibilities
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- Deliver unified triage across SCA/SAST/DAST findings: validate severity, assess exploitability, analyze false positives, and provide actionable fixes and escalations for production and business\-critical apps.
- Coordinate responses to threat intelligence escalations and PatchNow Critical events, including scoping, owner routing, mitigation guidance, tracking, and closure verification.
- Monitor and analyze newly disclosed and novel vulnerabilities—especially those accelerated by frontier\-model research—and produce actionable remediation briefs.
- Engineer, test, and implement AI\-assisted security tooling leveraging frontier models for vulnerability identification, code reasoning, triage acceleration, and analyst workflow automation with appropriate guardrails.
- Support evaluation and onboarding of new AI capabilities: proof\-of\-value execution, security testing, control validation, data handling review, model output evaluation, success metrics, and governance documentation.
- Strengthen software supply chain security: secure open\-source dependency intake, SBOM and component visibility, malicious package detection, dependency health assessment, and policy enforcement across dev, pipeline, and artifact workflows.
- Improve developer environment security: harden IDEs, plugins/extensions, package managers, code\-assist tools, and CI integrations against malicious code and compromised components.
Must\-Have Experience
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- 3\+ years of code scanning
- 3\+ years of open\-source scanning
- 3\+ years of dynamic and static scanning
Qualifications
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- Proven track record triaging SCA/SAST/DAST findings and driving high\-severity escalations (threat intel and critical patch events) to remediation and closure.
- Engineering background in scripting, automation, APIs, CI/CD workflows, developer tooling, or security platform integrations.
- Hands\-on familiarity with AI\-enabled security tools, frontier models, coding assistants, prompt/tool orchestration, model evaluation, or AI governance.
- Experience securing the software supply chain and developer tooling (IDEs, plugins/extensions, package managers, CI/CD integrations) from compromise and malicious code.
- Ability to convert technical vulnerabilities into clear remediation steps, risk summaries, and prioritized recommendations for dev and security stakeholders.
Proficiencies
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- Code Scanning
- DAST
- Open\-source scanning
- SAST
- SCA
- Dynamic and static scanning
Tools \& Technologies
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- CI/CD
- Artificial Intelligence
Salary Context
This $166K-$187K 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 Buckeye Global, 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 in Demand for This Role
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. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($176K) sits 19% below the category median. Disclosed range: $166K to $187K.
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.
Buckeye Global AI Hiring
Buckeye Global has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Canton, OH, US. Compensation range: $187K - $187K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 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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