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About This Role
College Board – Technology – Infrastructure & InfoSec
Location: This is a fully remote role. Candidates who live near CB offices have the option of being fully remote or hybrid (Tuesday and Wednesday in office).
Type: This is a full-time position
About the Team
The College Board Product Security team is a close-knit and enthusiastic group of technologists with a thirst for knowledge in security and cloud. We collaborate closely to investigate and solve problems and have strong alignment with our product teams to be a step ahead in securing the organization’s suite of products. We are an agile organization, embracing AI and cloud-native systems, and are focused on improving speed and security of service delivery in support of our important mission. Our team comes from a wide variety of backgrounds as well as experience within the security space and we work to ensure everyone on the team has a voice.
Product Security Engineers work closely with product and engineering teams as well as other organizational stakeholder groups to achieve product and security business objectives. They support threat modelling, architecture design, and implementation of secure development practices. In addition, they help define organizational security standards and organizational capability and tooling adoption decisions.
About the Opportunity
As a Senior Product Security Engineer, you will embed with assigned product and engineering teams to own security across the full development lifecycle, from feature-level design review and threat modeling through vulnerability remediation and production monitoring. This includes a heavy focus on securing AI features built into College Board's products, implementing AI-native security tooling and features, and assessing and mitigating the risks that AI development tools introduce into the software supply chain. You will join planning and grooming sessions as the team's security partner, translating compliance and requirements and security risks into guidance engineers can act on.
You will lead risk assessments, provide prioritization advice, and drive mitigation of risks. Alongside that product or engineering team work, you will coach developers on security practices, including the responsible use of AI coding tools.
This role is equal parts hands-on security engineering and organizational enablement. Your work will directly protect platforms that serve millions of students and educators worldwide.
In this role, you will:
Partner with Product and Engineering Teams (50%)
- Serve as the security partner for assigned product and engineering teams; join planning, grooming, and design sessions to advise on security architecture and flag risk early.
- Own threat models and risk assessments for assigned products; communicate security risks and mitigation plans to technical and nontechnical stakeholders.
- Own the security engagement lifecycle for each assigned product, from design review and documentation through production deployment and monitoring.
- Lead vulnerability reviews and remediation; assess exploitability to set accurate risk priority.
- Apply knowledge of emerging best practices in AI security, CB's security policies, and compliance requirements (SOC 2, ISO 27002, PCI, PII) to guide product teams.
- Write and review security design documentation and code; give and receive direct feedback in design reviews.
Ensure assigned products meet Product Security Framework requirements; identify gaps and drive remediation. *
Elevate Product Security (25%)
- Coach product teams and junior engineers through discussions, code reviews, and hands-on training to advance secure SDLC practices.
- Grow and mature the AI Product Security program; develop security controls and guidance in the use of AI in software engineering, in our products, and in our security program.
- Document and share emerging security patterns; keep your knowledge accessible to the full team.
Stay current with industry trends; apply what you learn to improve the partner program. *
Drive Operational Improvement (25%)
- Deploy and operate security tooling across cloud environments; integrate automated security controls into CI/CD pipelines.
- Contribute to roadmap planning and strategic direction: identify security risks and opportunities, escalate issues with cross-team scope, and synthesize feedback to inform priorities.
- Define and track KPIs that measure product security maturity and partner team posture; use the data to prioritize work and report progress.
- Participate in agile ceremonies; manage assigned epics and assess roadmap timelines and risk.
Work with the Information Security team on incident response and operational initiatives. *
About you, you have:
- 5–8 years of product security experience, including ownership of the full security engagement lifecycle—from threat modeling and architecture review through vulnerability remediation and production monitoring.
- Application security expertise across secure SDLC, CI/CD pipeline security, cloud environments (AWS preferred), and common frameworks including OWASP and CIS.
- Strong knowledge of emerging security trends in AI and agentic AI in software engineering and software products; the ability to identify industry patterns in AI risks and apply them to your day-to-day work.
- Knowledge of audit and compliance requirements (SOC 2, ISO 27002, PCI, PII), especially as they relate to the use of AI, and skill at translating them into clear guidance for engineering teams.
- Proficiency reviewing and improving application architecture for security, testability, and maintainability; hands-on experience with JavaScript/TypeScript or Python, and AWS is a plus.
- The ability to establish strong working relationships with product owners and engineering teams; experience shaping backlog prioritization and roadmap decisions from a security perspective.
- An emerging security strategy instinct: you identify risks and opportunities across team boundaries, start shaping security direction, and communicate implications clearly.
- Expertise in more than one technical domain and a commitment to keeping knowledge documented and shared, so the team never relies on one person.
- Evidence-based judgment in candidate evaluation; consistent standards across resumes and interviews.
Ability to travel 3–5 times per year to College Board offices in New York or Reston, VA. *
All roles at College Board require:
- A passion for expanding educational and career opportunities and mission-driven work
- Authorization to work in the United States for any employer
- Curiosity and enthusiasm for emerging technologies, with a willingness to experiment with and adopt new AI-driven solutions and comfort learning and applying new digital tools independently and proactively.
- Clear and concise communication skills, written and verbal
- A learner's mindset and a commitment to growth: welcoming diverse perspectives, giving and receiving timely, respectful feedback, and continuously improving through iterative learning and user input.
- A drive for impact and excellence: solving complex problems, making data-informed decisions, prioritizing what matters most, and continuously improving through learning, user input, and external benchmarking.
A collaborative and empathetic approach: working across differences, fostering trust, and contributing to a culture of shared success. *
About Our Process
- Application review will begin immediately and will continue until the position is filled. This role is expected to accept applications for a minimum of 5 business days.
While the hiring process may vary, it generally includes: resume and application submission, recruiter phone/video screen, hiring manager interview, performance exercise, a panel interview, a conversation with leadership and reference checks. *
What We Offer
At College Board, we offer more than just a paycheck, we provide a meaningful career, a supportive team, and a comprehensive package designed to help you thrive. We’re a self-sustaining nonprofit that believes in fair and competitive compensation, grounded in your qualifications, experience, impact, and the market.
A Thoughtful Approach to Compensation
- The hiring range for this role is $153,000 - $166,000
- Your exact salary will depend on your location, experience, and how your background compares to others in similar roles at the College Board.
- We aim to make our best offer upfront, rooted in fairness, transparency, and market data.
We adjust salaries by location to ensure fairness, no matter where you live. *
You’ll have open, transparent conversations about compensation, benefits, and what it’s like to work at College Board throughout your hiring process. Check out our careers page for more.
#LI-Remote
#LI-TheAcorn
Salary Context
This $153K-$166K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At College Board, 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($159K) sits 25% below the category median. Disclosed range: $153K to $166K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
College Board AI Hiring
College Board has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $166K - $166K.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 positions. About 6% 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 Safety roles lead at $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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