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About This Role
College Board – Technology – Platform Operations
Location: This is a remote role. Candidates who live near CB offices have the option of being fully remote or hybrid (Tuesday and Wednesday in office). All CB employees are required to occasionally travel to meet in person for business purposes.
Role Type: This is a full-time position
About the Team
The Platform Operations team at College Board builds solutions to help reimagine and elevate engineering practices around cloud infrastructure, architecture, networking, CI/CD, IaC, and developer experience. We operationalize the platforms that are core to our development environment within our growing AWS ecosystem. We also lead the organization's approach to agentic and AI-assisted software development, helping engineering teams safely and effectively adopt AI coding agents and tools. Our team consists of engineers with a unique blend of skills and experiences that foster a collaborative working environment where each individual member contributes to the success of the team. We learn and teach. We are creative and value the merit of ideas.
About the Opportunity
As a Senior Engineer focused on Agentic Engineering Governance for Open Source software, you are an idea-generator, a catalyst, and a collaborator who sets the standard for how College Board engineers use AI coding agents and agentic development tools safely, responsibly, and effectively. You understand cloud software delivery, tools, and processes that empower efficient and resilient delivery, and you bring that same rigor to the guardrails, policies, and standards that govern how developers use AI coding assistants (such as Claude Code, GitHub Copilot, and similar agentic tools). You are technologically curious and seek opportunities to research emerging technologies and trends, standards, and products. Your eagerness and vision enable you to create and learn complex solutions.
Your focus will be on the fast-moving area of agentic governance for open-source software, where you build the program for license compliance, dependency risk, and review of the code we publish externally. You thrive in an environment with a strong mix of creativity and productivity, splitting your time between hands-on building and setting the standards others follow. You work closely with Security, Legal, and the developers who rely on us. Your excellent communication and mentoring skills allow you to effectively articulate solutions and policies to the team, enabling all to make healthy, well-governed contributions. The result is that the teams building for 7 million students each year can adopt AI with confidence.
In this role, you will:
Design & Implementation (60%)
- Design and implement guardrails, permission models, and policy-as-code controls that govern how AI coding agents (e.g., Claude Code, GitHub Copilot, and similar agentic tools) interact with source code, infrastructure, and production systems
- Build solutions using the best practices of Agile, CI/CD, and DevOps
- Build and maintain tooling for sandboxing, audit logging, and rollback of agent-driven code and infrastructure changes
- Continuously develop the skills required to work as part of the agile team in a poly-skilled development environment, including hands-on use of agentic coding tools
- Be a subject matter expert in current technological capabilities such as cloud networking, API integrations, infrastructure resiliency, cloud design, and agentic/AI-assisted development patterns
- Write code to automate the infrastructure lifecycle, create and support internal tools utilizing IaC, and be able to use CLIs
- Evaluate and pilot emerging agentic programming tools, frameworks, and protocols (e.g., MCP, agent orchestration frameworks), and make adoption recommendations
- Partner with domain SMEs (Delivery Team Software Engineers, Security, Infrastructure, Network, Observability and Operations) to analyze features, capabilities, and risks of agentic tooling
- Contribute to the development and growth of the Cloud Engineering Program, through thought leadership, design and implementation of tools and processes
- Participate in the Enterprise Architecture Review process, with a focus on proposals involving AI coding agents and agentic tooling
Governance & Strategy (25%)
- Establish and maintain organization-wide standards, guardrails, and acceptable-use policies for agentic and AI-assisted software development
- Partner with Security, Legal/Compliance, and Architecture Review stakeholders to assess risk and approve new use cases for AI coding agents
- Contribute to technology strategy and planning; conduct research on emerging agentic and AI development technologies and recommend approaches that increase safety, cost effectiveness, and systems flexibility
- Author documentation, playbooks, and governance frameworks for developers using AI coding assistants
- Participate in agile scrum ceremonies (Sprint Planning, Grooming, Daily SCRUM, Demo, Retro) and contribute to team deliverables
- Participate in peer reviews of software engineering artifacts, including code produced or assisted by AI agents
- Assist in the identification and resolution of production and production-like issues, including those arising from agentic tooling, as part of the team’s daytime on-call rotation
Team Coordination & Enablement (15%)
- Mentor team members on the safe, effective, and compliant use of agentic coding tools
- Design and deliver training materials and office hours to help developers adopt agentic engineering practices
- Lead technical efforts and mentor technical staff in the development and usage of technology architectures and solutions, with a primary focus on agentic engineering governance for open source software
- Serve as an escalation point for questions on AI coding tool policy and governance
About You
To qualify for this role, you must have:
- 5+ years of experience with Infrastructure as Code, CI/CD framework, immutable infrastructure, automation, orchestration, and other modern DevOps patterns
- Significant hands-on experience with AI coding agents or assistants (e.g., Claude Code, GitHub Copilot, Cursor, Windsurf) or other LLM-based developer tooling
- Experience with LLM platforms and APIs such as Anthropic Claude or OpenAI, including agent orchestration and tool-use concepts like function calling and MCP
- Understanding of AI/LLM security considerations such as prompt injection, data exfiltration, permission scoping, and sandboxing
- Experience establishing technical standards, guardrails, or review processes across an engineering organization, in responsible AI governance or an adjacent discipline such as security
- Technical knowledge of Cloud Computing, DevOps, and Microservices, with strong experience in distributed cloud infrastructure (AWS preferred, with experience in services such as Lambda, SNS, SQS, S3, Step Functions, EC2, ECS, VPC, IAM, CloudWatch, and DynamoDB a plus)
- Experience building event-driven cloud-based serverless applications
- A strong background in a language such as Python, JavaScript, React.js/Node.js, or Java, with the logic and problem-solving skills to pick up new languages quickly
- Excellent communication, collaboration, and documentation skills; ability to present ideas in business-friendly and user-friendly language, including to non-technical stakeholders on AI governance topics
- Experience supporting developers as internal customers, gathering their requirements and feedback, and earning adoption of the standards you set
- Curiosity and discipline to research emerging technologies, standards, and products, and the ability to prioritize and execute in a high-pressure environment
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 such as live coding, 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 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.
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Salary Context
This $153K-$166K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 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 $212,040 based on 13,780 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($159K) sits 25% below the category median. Disclosed range: $153K to $166K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
College Board AI Hiring
College Board has 2 open AI roles 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 $188,200 across 2,430 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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.
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