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Values & Innovation
At Under Armour, we are committed to empowering those who strive for more, and the company's values - Act Sustainably, Celebrate the Wins, Fight on Together, Love Athletes and Stand for Equality - serve as both a roadmap for our teams and the qualities expected of every teammate.
Our Values define and unite us, the beliefs that are the red thread that connects everyone at Under Armour. Our values are rallying cries, reminding us why we're here, and fueling everything we do.
Our pursuit of better begins with innovation and with our team's mission of being the best. With us, you get the freedom to go further - no matter your role. That means developing, delivering, and selling the state-of-the-art products and digital tools that make top performers even better.
If you are a current Under Armour teammate,
Purpose of Role
As a Lead Application and AI Security Engineer in our Digital Product Security organization, you will secure Under Armour’s portfolio of Direct-to-Consumer & Business-to-Business applications. As a secure software and AI security leader, you will drive secure-by-design development practices, establish security guardrails for AI-enabled solutions, and advance AI-powered security capabilities across our software development lifecycle. You will also build AI-based security capabilities and leverage industry-leading solutions to protect our customer applications and data spanning web applications, mobile applications, and application programming interfaces (APIs).
Your Impact
- Define and execute our application and AI security strategy, policies, and roadmap across all of our software development teams
- Lead the integration of AI-powered security capabilities across the software development lifecycle
- Partner with engineering teams to embed security and AI controls into developer coding platforms, CI/CD pipelines, and applications
- Serve as the technical lead for AI security initiatives across web, mobile, API, and third-party AI service consumption
- Develop application and AI security standards for technology that consumes, integrates, or builds AI/ML capabilities
- Establish governance, guardrails and security requirements for the development, deployment, and operation of GenAI, LLMs, and agentic solutions
- Evaluate, pilot, and operationalize emerging AI security technologies
- Define acceptable use policies, code review requirements, data protection controls, and validation processes for AI-generated code
- Mentor developers, architects, and engineering leaders on secure application development practices
Qualifications
- Bachelor's degree in computer science/IT/engineering or a related discipline with typically 8 years of cyber security experience or Master's degree with typically 6 years of relevant experience or typically 12 years of relevant work experience without degree
- Understanding of how modern engineering teams build software using AI coding assistants, code generation platforms, and AI-powered services
- Requires 5+ years of application security experience
- Strong familiarity with OWASP Top 10, OWASP API Security Top 10, OWASP Top 10 for LLM Applications, and secure AI software development practices
- Strong familiarity with secrets detection, SAST, DAST, SCA, & API security testing
- Experience securing AI-enabled applications, including LLMs, AI agents, MCP integrations, prompt injection defenses, model governance, and data protection controls
- Experience using AI to code scripts that leverage APIs to build custom application security solutions
- Strong cross-functional teaming skills to coordinate and implement security enhancements with product and engineering teams
Workplace Location
- Location: Candidates must reside within a commutable distance to our corporate headquarters in Baltimore, Maryland, as periodic on-site attendance may be required
- Work Schedule: Remote with periodic on-site attendance
- Travel: 1-2 times per year
- Sponsorship Eligibility: UA does not offer sponsorship of job applicants for employment-based work authorization for this position at this time.
Relocation
No relocation provided
Base Compensation
$155,000.00-$200,000.00 USD
Most new hires fall within this range and have the opportunity to earn more over time. Initial placement within the salary range, however, is based on an individual's relevant knowledge, skills and experience for the position. UA is committed to helping our teammates succeed and advance in their careers. Base salary is only one component of our competitive Total Rewards package.
Benefits & Perks
- Paid "UA Give Back" Volunteer Days: Work alongside your team to support initiatives in your local community
- Under Armour Merchandise Discounts
- Competitive 401(k) plan matching
- Maternity and Parental Leave for eligible and FMLA-eligible teammates
- Health & fitness benefits, discounts and resources- We offer teammates across the country programs to promote physical activity and overall well-being
Our Commitment to Equal Opportunity
At Under Armour, we are committed to providing an environment of mutual respect where equal employment opportunities are available to all applicants and teammates without regard to race, color, religion or belief, sex, pregnancy (including childbirth, lactation and related medical conditions), national origin, age, physical and mental disability, marital status, sexual orientation, gender identity, gender expression, genetic information (including characteristics and testing), military and veteran status, family or paternal status and any other characteristic protected by applicable law. Under Armour seeks to recruit, develop and retain the most talented people representing a wide variety of backgrounds and perspectives. If a reasonable accommodation is needed to participate in the job application or interview process, please contact our Human Resources team via [email protected].
Requisition ID: 167356 Location: Baltimore, MD, US, 21230 Business Unit: Corporate Region: North America Employee Class: Full Time Employment Type: Salaried
Salary Context
This $155K-$200K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Under Armour, 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 in Demand for This Role
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 $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($177K) sits 16% below the category median. Disclosed range: $155K to $200K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Under Armour AI Hiring
Under Armour has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Baltimore, MD, US. Compensation range: $200K - $200K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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