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About This Role
WORK OPTION: Remote
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Position Summary:
As a Senior Manager in League Office CyberSecurity department, the Gen AI Security \& DevSecOps Engineer builds and operates the security infrastructure, automation, and governance that keep the NBA's software delivery and AI adoption secure. The role spans three domains: DevSecOps (securing the CI/CD pipelines and software development lifecycle that ship NBA applications), Gen AI Security (securing the organization's growing use of Generative AI, agents, and AI developer tools), and Cloud Security (securing the cloud, Kubernetes, and secrets posture that those applications run on). This is a hands\-on engineering role: the candidate designs DevOps processes, administers and builds security tooling across the software development lifecycle, embeds security testing and policy enforcement into the pipeline, and owns security review and runtime controls for AI.
Major Responsibilities
- Secure CI/CD pipelines at scale across the organization's CI/CD platforms with standardized security templates and automated policy enforcement, embedding static analysis (SAST), software composition analysis (SCA), container, infrastructure as code (IaC), and secrets scanning, with break build enforcement on critical and high severity findings.
- Administer the enterprise SAST and SCA platform (scan engine infrastructure, query tuning, severity calibration, finding triage) and maintain the exploitability knowledge base that distinguishes true positives from false positives to keep security fast and low friction.
- Build automated compliance tooling that detects required scans, validates pipeline configuration, and flags coverage gaps; audit pipeline posture across platforms and drive remediation directly with engineering teams.
- Support secure SDLC practices and security gates (SAST, SCA, container, IaC, DAST), threat modeling, SBOM generation, and dependency verification; coordinate with the DAST and penetration testing functions and act on bug bounty findings.
- Design and build the enterprise security operations platform and the automation that orchestrates DevSecOps workflows (scan state changes, triage, exemptions, intake, notifications), including AI\-assisted vulnerability triage with appropriate guardrails, audit trails, and human oversight.
- Define and report security risk metrics, lead security audits and assessments, conduct supply chain and CVE incident response across the estate, and mentor junior team members.
- Lead security reviews of Generative AI applications, agentic workflows, and AI developer tools submitted through the enterprise AI intake process, assessing against OWASP Top 10 for LLM Applications, OWASP Top 10 for Agentic Applications, NIST AI RMF, MITRE ATLAS, and NBA Gen AI security policy and standards.
- Author and maintain NBA Generative AI security policy and standards, including controls for AI data protection, model and prompt security, agentic AI, MCP (Model Context Protocol) integration, and AI developer tooling.
- Evaluate and onboard Gen AI security tooling such as runtime guardrails, AI red teaming, browser and DLP controls, and MCP governance, and design and operate runtime AI security controls integrated into application pipelines and runtime.
- Govern enterprise security over AI developer tooling and the MCP server approval workflow, and partner with the Enterprise Gen AI, Cloud Infrastructure, GRC, Legal, and Privacy functions to align AI security controls with broader AI governance.
- Administer and operate the enterprise cloud security platform across a large multi\-cloud estate (cloud posture, container, and IaC scanning); detect, prioritize, and drive remediation of misconfigurations and vulnerabilities against defined SLAs with infrastructure and application teams.
- Own cloud security scanning policy configuration and service account governance (scope, least privilege, credential rotation), lead platform lifecycle work, and perform technical security configuration assessments of cloud platforms.
- Own the Kubernetes security posture across a large cluster footprint, including admission control, RBAC, namespace isolation, network policies, and Pod Security Standards, and execute admission controller enforcement programs that move policies from audit to block in staged, owner\-communicated rollouts with exception and rollback processes.
- Design and operate automated credential rotation across cloud identity and key management services (cross\-account role assumption, grace periods, owner notifications) and lead the initiative to eliminate static access keys in favor of OAuth 2\.0, OIDC, and role\-based authentication.
- Manage the secrets lifecycle across cloud and pipeline secret stores with detection, alerting, and automated rotation, and build reporting that surfaces aging and non\-compliant secrets.
Required Education \& Professional Experience
- Bachelor's degree in a technical discipline (or equivalent work experience).
- Minimum of seven years in IT (a minimum of five years in information security).
- Hands\-on experience administering and integrating modern security tooling across the DevSecOps toolset, including SAST, SCA, DAST, container, IaC, and secrets scanning.
- Hands\-on experience designing and securing CI/CD pipelines on modern CI/CD platforms.
- Strong programming and scripting ability, with the ability to build integrations, automation, and tooling against platform APIs.
- Solid hands\-on implementation of cloud security across at least one major cloud provider, including identity and access management, secrets management, network security, and posture management, guided by frameworks such as CIS Benchmarks, Cloud Security Alliance, and the NIST SP 800\-53 and 800\-190/800\-204 series.
- Working knowledge of Kubernetes and container security, including RBAC, admission control, namespace isolation, network policies, and Pod Security Standards.
- Familiarity with AI and LLM security frameworks (OWASP Top 10 for LLM Applications, OWASP Top 10 for Agentic Applications, NIST AI RMF, MITRE ATLAS) and the controls that mitigate Generative AI risks such as prompt injection, sensitive data disclosure, and excessive agency (preferred).
- Understanding of governance applied to cloud and AI computing in terms of risk, exposure, impact, and policy; experience writing architectural plans, standards, and guidelines for enterprise platforms.
- One or more industry security certifications, such as GIAC (GCSA), a cloud security certification (CCSP, CCSK, or a cloud provider security certification), or an application or offensive certification (CEH, OSCP, or CySA\+).
Salary Range:
$145,000\-$165,000
Job Posting Title:
Senior Manager
Employees currently are eligible to receive an annual discretionary performance bonus, awarded at the sole discretion of the Company and subject to any terms and conditions set by the Company. Employees and/or eligible dependents may be eligible to participate in the following Company\-sponsored employee benefit programs: medical; dental; vision; life/AD\&D insurance; short\- and long\-term disability; fertility and family\-forming assistance; wellbeing allowance; educational assistance; mental health coaching/therapy; tax advantaged accounts such as HSA and healthcare/dependent care FSAs; a 401(k) retirement plan; and time off benefits that include vacation, sick time, and personal days.
We Consider Applicants For All Positions On The Basis Of Merit, Qualifications And Business Needs, And Without Regard To Race, Color, National Origin, Religion, Sex, Gender Identity, Age, Disability, Alienage Or Citizenship Status, Ancestry, Marital Status, Creed, Genetic Predisposition Or Carrier Status, Sexual Orientation, Veteran Status, Familial Status, Status As A Victim Of Domestic Violence Or Any Other Status Or Characteristic Protected By Applicable Federal, State, Or Local Law.
The NBA is committed to providing a safe and healthy workplace. To safeguard our employees and their families, our visitors, and the broader community from COVID\-19, and in consideration of recommendations from health authorities and the NBA’s own advisors, any individual working onsite in our New York and New Jersey offices must be fully vaccinated against COVID\-19\. The NBA will discuss accommodations for individuals who cannot be vaccinated due to a medical reason or sincerely held religious belief, practice, or observance.
About the NBA
The National Basketball Association (NBA) is a global sports and media organization with the mission to inspire and connect people everywhere through the power of basketball. Built around five professional sports leagues: the NBA, WNBA, NBA G League, NBA 2K League and Basketball Africa League, the NBA has established a major international presence with games and programming available in 214 countries and territories in 60 languages, and merchandise for sale in more than 200 countries and territories on all seven continents. NBA rosters at the start of the 2024\-25 season featured a record\-tying 125 international players from a record\-tying 43 countries. NBA Digital’s assets include NBA TV, NBA.com, the NBA App and NBA League Pass. The NBA has created one of the largest social media communities in the world, with more than 2\.3 billion likes and followers globally across all leagues, team and player platforms. NBA Cares, the NBA’s global social responsibility platform, partners with renowned community\-based organizations around the world to address important social issues in the areas of education, inclusion, youth and family development, and health and wellness.
Salary Context
This $145K-$165K 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 National Basketball Association, 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. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($155K) sits 29% below the category median. Disclosed range: $145K to $165K.
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.
National Basketball Association AI Hiring
National Basketball Association has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Secaucus, NJ, US. Compensation range: $165K - $165K.
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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