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About This Role
ISO New England is the independent system operator responsible for ensuring the safe and reliable flow of electricity in our region and planning for the future of the electric grid. We are at the forefront of New England’s ongoing transition to clean energy.
ISO New England is seeking an experienced Cyber Security Analyst to support and enhance enterprise cloud, infrastructure, AI, and DevSecOps security initiatives. This role is responsible for proactively identifying vulnerabilities, compliance gaps, misconfigurations, and security risks across enterprise systems, cloud platforms, AI/ML environments, CI/CD pipelines, and regulated CIP environments.
The ideal candidate will have strong experience in cloud security, vulnerability management, DevSecOps, AI security, and security compliance, with the ability to collaborate across technical teams to implement and maintain enterprise security standards.
What we offer you:
- A stable, mission\-driven workplace where your impact truly matters
- A highly engaged work environment that values inclusion, collaboration, and employee safety and wellbeing
- Competitive compensation with a base salary \+ performance bonus
- Robust benefits package, including:
- Enhanced 401(k) and financial planning support
- Tuition reimbursement and professional development
- Wellness programs, including an onsite gym
- Flexible work hours
- Employee Business Networks
- Free coffee at our onsite café
- Hybrid work environment (3 days/week onsite)
- Distance\-based relocation assistance available
- 5/6 person paid on\-call rotation
How you will make an Impact
- Conduct cloud security assessments across AWS and Azure environments, including CSPM, CIEM, IAM, and container security reviews.
- Integrate and support security controls within CI/CD pipelines and DevSecOps environments.
- Assess and secure AI/ML systems, generative AI applications, and AI\-enabled business solutions throughout their lifecycle.
- Evaluate risks associated with AI model usage, training data, third\-party AI services, and Large Language Model (LLM) integrations.
- Implement AI governance controls to protect sensitive data and prevent unauthorized disclosure through AI platforms.
- Develop security guardrails for AI adoption, including data classification, access controls, prompt security, and responsible AI usage.
- Perform AI threat modeling to identify risks such as prompt injection, data poisoning, model manipulation, model theft, and insecure AI integrations.
- Perform vulnerability assessments, configuration reviews, and remediation tracking across enterprise and CIP systems.
- Review and validate baseline configurations, logging requirements, and compliance controls.
- Evaluate network topology and infrastructure changes to determine CIP impact and SOC visibility requirements.
- Partner with application development, cloud platform engineering, infrastructure, enterprise architecture, IAM, network, SOC, and business teams to integrate security into system design, projects, and operational processes.
- Support phishing simulations, security awareness initiatives, AI security awareness training, and audit evidence collection.
- Provide security recommendations and risk mitigation strategies for cloud, AI, and enterprise environments.
- Support and maintain enterprise security platforms including CNAPP, EDR, vulnerability management, SIEM, DSPM, and cloud security monitoring tools.
- Monitor evolving AI security risks, industry standards, and regulatory requirements.
What we are looking for
- Experience in cybersecurity, cloud security, security engineering, or AI security roles.
- Experience with AWS and/or Azure cloud platforms.
- Experience with container orchestration technologies including Amazon ECS and Amazon EKS.
- Experience implementing security controls within CI/CD and DevSecOps environments.
- Knowledge of AI security concepts, including securing generative AI applications, LLMs, AI governance, and AI risk management.
- Familiarity with AI threats such as prompt injection, data leakage, model poisoning, model theft, and insecure AI APIs.
- Knowledge of vulnerability management, cloud security, IAM, CSPM, and configuration management practices.
- Experience with security monitoring and logging platforms.
- Familiarity with Terraform, infrastructure\-as\-code, and policy governance frameworks such as OPA.
- Understanding of data governance, data protection, and responsible AI principles.
- Strong analytical, troubleshooting, communication, and collaboration skills.
- Self\-starter with the ability to work independently in a fast\-paced environment.
Desired not required
- Bachelor’s degree in information technology, Cybersecurity, Computer Science, or related field.
- Advanced degree such as a Master’s in Cybersecurity Management, Information Assurance, Artificial Intelligence, or related fields.
- Industry cybersecurity, cloud security, and AI security certifications preferred, including:
- ISC2 Certified Information Systems Security Professional (CISSP)
- ISACA Certified Information Security Manager (CISM)
- Amazon Web Services Certified Security – Specialty
- Microsoft Azure Security Engineer Associate (AZ\-500\)
- ISC2 Certified in AI Security (when available)
- OWASP AI Security and LLM Security knowledge/training
This employer will not sponsor applicants for work visas for this position (ex: H\-1B, F\-1/CPT/OPT, O\-1, E\-3, TN, J, etc.).
The expected salary range for this position is $127,000 \- $150,000 per year. This role is also eligible for an annual performance bonus, comprehensive health insurance (medical, dental and vision), flexible spending and health savings accounts, a 401(k) plan with generous employer contributions and a student debt benefit, life and AD\&D insurance, disability insurance, critical illness and hospital indemnity benefits, paid time off, paid leave, a wellness program, an employee assistance program and other great company perks.
\#LI\-HYBRID
This is a U.S. based role. If the successful candidate resides outside of the U.S., relocation will be required.
Equal Opportunity: We are proud to be an EEO employer. Applicants for employment are considered without regard to race, color, religion, creed, sex (including pregnancy, childbirth, and related medical conditions), gender identity or expression, sexual orientation, citizenship, national origin, age, ancestry, marital status, disability (including learning, mental, intellectual, and physical), service in the uniformed services, genetic information, or any other status protected by applicable law.
Drug Free Environment: We maintain a drug\-free workplace and perform pre\-employment substance abuse testing.
Salary Context
This $127K-$150K 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 ISO New England, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($138K) sits 37% below the category median. Disclosed range: $127K to $150K.
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.
ISO New England AI Hiring
ISO New England has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Holyoke, MA, US. Compensation range: $150K - $150K.
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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