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About This Role
About the Role
We are looking for a savvy, high\-performing Principal AI Threat Detection Engineer – Insider Threat to lead the strategy, design, and day\-to\-day management of Blackbaud’s Insider Threat program as it matures into an AI\-augmented capability, protecting Blackbaud’s and our clients’ information. As a technical leader within Security Engineering, this individual will direct AI\-driven and agentic tooling to investigate anomalous events and alerts, detect malicious and anomalous insider activity, and reverse engineer malware, while personally applying the contextual judgment, escalation authority, and accountability that AI cannot provide on its own.
The Principal AI Threat Detection Engineer serves as the subject matter expert for Insider Threat tooling, User and Entity Behavior Analytics (UEBA), Security Orchestration Automation and Response (SOAR) platforms, and the AI/ML models underpinning them, validating model outputs, tuning detection logic, and partnering with leadership to report on the business impact of theft, destruction, alteration, or denial of access to information. As the program shifts more first\-pass analysis to AI, this role increasingly focuses human effort where it matters most: resolving ambiguous or high\-stakes cases, overseeing AI\-assisted decisions, and troubleshooting complex threats that impact the information security infrastructure at the data, application, service, operating system, and network levels.
What You’ll Be Doing
- Lead the maturation of Blackbaud’s Insider Threat detection program toward an AI\-augmented capability, including administration, tuning, and optimization of Insider Threat tools, UEBA platforms, and the AI/ML models underneath them
- Perform intrusion and insider risk analysis using SIEM technology, UEBA behavioral analytics, AI\-generated insights, reports, data visualization, log analysis, and pattern analysis, applying human judgment to validate AI findings and separate true signal from noise
- Direct AI\-driven hunting tools and agents to identify threat actor groups (external and insider) and their respective tactics, techniques, and procedures, personally leading the investigation of cases that require nuanced human judgment
- Serve as the human escalation point and first responder for security events that AI and automated tooling flag but cannot fully resolve, via email, phone, and tickets across corporate user networks, data centers, and cloud environments
- Own remediation of information security incidents, including insider threat investigations, ensuring AI\-assisted findings are verified and contextualized before action is taken
- Document and communicate findings, escalate critical incidents, and interact with lines of business, HR, Legal, and other stakeholders on sensitive insider matters, exercising the discretion and judgment that AI systems cannot provide
- Design and build AI\-driven and SOAR\-based automated workflows within Detection Engineering, defining the guardrails, escalation thresholds, and human checkpoints that keep automation safe, effective, and improve analyst performance
- Champion responsible use of AI\-driven analysis and automation to enhance detection accuracy and accelerate triage, while evaluating model accuracy, bias, and drift to keep detection logic explainable and trustworthy
- Document automation and AI model deployment processes, to include defining pre\-build requirements, validation criteria, and human review checkpoints for high\-risk decisions
- Utilize AI and automation to build metrics and dashboards supporting the Insider Threat and broader detection program, applying human interpretation to translate outputs into decisions leadership can act on
- Serve as a thought leader on the evolving division of labor between AI and human analysts – determining which decisions should be automated, which require human review, and how that boundary should shift as the program matures – as well as on new alert content, data correlation, and anomaly thresholds
- Improve and challenge existing processes and procedures in a very agile and fast\-paced cyber security environment
- Keep current on the threat landscape, insider risk trends, cyber security developments, and emerging AI/ML techniques relevant to detection engineering
- Adapt to fluid infrastructures, and evaluate, pilot, and integrate new AI\-enabled technologies and platforms
- Act as peer reviewer and technical escalation point within the core security engineering team, including reviewing AI\-assisted detection logic and automation before deployment
- Advise and inform leadership on how to optimize the current toolset and evaluate future tools, including UEBA, SOAR, and AI\-enabled analysis platforms, recommending where automation should expand and where human oversight must remain
What We’ll Want You to Have
- 8\+ years of Security Engineering and Analysis experience, preferably in Threat Detection and Response, including demonstrated experience supporting Insider Threat programs
- 5\+ years of IT or networking experience
- Hands\-on experience administering, tuning, and optimizing Insider Threat detection tools, User and Entity Behavior Analytics (UEBA) platforms, and the AI/ML models that power them
- Experience with SOAR tools and playbook development, including explicit expertise applying AI\-driven and agentic automation to security operations and critically evaluating AI\-generated findings for accuracy
- Experience with security metrics and reporting, preferably including automating recurring metrics and reporting processes
- Demonstrated ability to critically evaluate AI/ML model outputs
- Intermediate to Advanced Linux/Unix OS and Windows knowledge
- Firewall rule and policy, and network routing fundamentals
- Ability to manage parallel tasks and accurately document resolutions
- Proven ability to implement automation through scripting (e.g., PowerShell, PERL, Python, Bash scripting), including integrating AI/ML APIs or agentic frameworks into detection workflows
- Experience leveraging APIs to integrate third\-party tooling into an existing tool stack
- Familiarity with cyber security frameworks such as NIST and MITRE ATT\&CK, and awareness of emerging AI governance and security frameworks (e.g., NIST AI RMF, MITRE ATLAS)
- Industry\-recognized professional certification such as Security\+, CBROPS, CSA, CEH, GSEC, SSCP
What We’ll Prefer You Have
- CISSP, GBFA, GCDA, GCIA, GCIH, GMON, GNFA, GOSI, GPEN, GPPA, GREM, GSOC, OSDA, OSCP
- Working knowledge of network packet analysis tools
- Direct experience with malware and analysis techniques and methodologies
- Prior experience leading or mentoring a security engineering or analyst team through periods of technology and process change, including adoption of AI\-driven tooling
- Experience presenting insider risk metrics and program updates to senior leadership
- Experience partnering with data science or AI/ML engineering teams to build, validate, or govern detection models
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Blackbaud powers social impact through purpose‑driven technology and responsible AI. Guided by our *Intelligence for Good®* vision, we’re building a culture where innovation, trust, and human expertise come together to help organizations make a greater difference in the world.
Blackbaud is proud to be an equal opportunity employer and is committed to maintaining a diverse and inclusive work environment. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, physical or mental disability, age, or veteran status or any other basis protected by federal, state, or local law.
The starting base pay is $117,200\.00 to $157,500\.00\. Blackbaud may pay more or less based on employee qualifications, market value, Company finances, and other operational considerations.
Benefits Include:
- Medical, dental, and vision insurance
- Remote\-flexible workforce
- Wellness Programs
- 401(k) program with employer match
- Flexible paid time off
- Generous Parental Leave
- Donations for Doers
- Pet insurance, legal and identity protection
- Tuition reimbursement program
Salary Context
This $117K-$157K range is below the median 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 Blackbaud, 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($137K) sits 36% below the category median. Disclosed range: $117K to $157K.
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.
Blackbaud AI Hiring
Blackbaud has 5 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $114K - $157K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% 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 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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