Interested in this AI/ML Engineer role at Zebra Technologies?
Apply Now →About This Role
Overview:
At Zebra, we are a community of innovators who come together to create new ways of working. United by curiosity and a culture of caring, we develop smart solutions that anticipate our customer’s and partner’s needs and solve their challenges.
Being part of Zebra Nation means you are seen, heard, valued, and respected. Drawing from our unique perspectives, we collaborate to deliver on our purpose. Here you are part of a team pushing boundaries today to redefine the work of tomorrow for organizations, their employees, and those they serve.
You’ll have opportunities to learn and lead in a forward\-thinking environment, defining your path to a fulfilling career while channeling your skills toward causes you care about—locally and globally.
Come make an impact every day at Zebra.
What We're Looking For:
The Director, AI Enablement is responsible for enabling consistent, scalable, and responsible internal AI adoption across Product \& Solutions (P\&S).
This role establishes the operating model, alignment mechanisms, and practical guardrails that allow P\&S organizations to independently drive AI enabled improvements while ensuring those efforts align with enterprise requirements and P\&S priorities.
The role requires deep understanding of product development and delivery lifecycles and the ability to actively engage with execution teams to shape how AI is applied, measured and scaled.
Essential Duties and Responsibilities:
- Establish and maintain a clear, repeatable operating model for internal AI adoption across P\&S, ensuring AI initiatives consistently address workflow design, risk considerations, reuse opportunities, adoption planning, and outcome measurement.
- Provide portfolio level visibility into AI initiatives across P\&S, actively engaging with execution teams to identify duplication, conflicting approaches, and opportunities to scale effective solutions.
- Maintain a reuse register capturing AI patterns, workflows, agents, vendors, and lessons learned, and actively guide teams toward reuse where it improves scale, cost, or consistency.
- Translate enterprise AI governance, security, legal, and regulatory requirements into practical, actionable expectations for P\&S teams, working directly with execution leaders to integrate requirements into existing workflows.
- Serve as an active thought partner to PMO, Engineering, and Product leaders, helping teams reason through how AI should be applied, where it adds value, and how success should be measured in the context of their workflows.
- Establish a common measurement framework for AI adoption across P\&S and work with execution teams and analytics partners to enable consistent visibility into adoption, productivity, and business outcomes using team\-defined metrics.
- Lead and develop an internal business intelligence team, guiding their evolution toward predictive analytics and foundational data readiness to support the broader AI operating model
- Capture successful AI practices emerging from execution teams and enable reuse through shared guidance, reference examples, and cross\-team knowledge sharing.
- Drive alignment and outcomes across P\&S through influence, partnership, and guidance, enabling teams to independently execute AI\-enabled improvements within shared standards.
- Act as a trusted advisor to P\&S leadership by synthesizing AI adoption trends, outcomes, and maturity to support executive decision\-making and cross\-functional alignment.
- Identify and escalate alignment issues, at both the team and organizational level, that create material risk, cost, or scalability concerns for AI adoption across P\&S.
Minimum Qualifications:
- Bachelor’s degree in Engineering, Computer Science, Data Science, or a highly quantitative related field.
- Minimum 10\+ years of progressive leadership experience in product development, digital transformation, data science/analytics or related domains with significant exposure to AI enabled initiatives
- Minimum of 5\+ years’ operating as part of a product development team.
- Must have experience delivering transformation initiatives spanning multiple business units or global geographies, supported by verifiable metrics, such as number users impacted, and percent efficiency gains.
Must have direct people management experience leading and developing data analytics, business intelligence (BI), engineering, data science, or technical program/product management teams.
*
Preferred Qualifications:
- Master’s degree (MBA or equivalent).
- Leadership of at least one significant cross\-functional GenAI or automation initiative focused on modernizing operational workflows.
- Deep understanding of product development and delivery lifecycles, with the ability to engage credibly with Engineering, Product, and PMO leaders.
- Proven ability to translate cross functional initiatives into measurable business impact and productivity improvements.
- Strong understanding of data architecture principles and what constitutes “AI data readiness” at enterprise scale.
- Strong track record of influencing outcomes and driving alignment without direct authority.
- Demonstrated experience communicating complex topics clearly and effectively to executive\-level audiences.
- Strong understanding of modern machine learning techniques and large language models, with the ability to guide decision making across data, modeling, engineering, and cloud platform teams.
- Familiarity with enterprise governance, risk, security, or regulatory environments.
Equal Opportunity Employer:
Zebra is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability and protected veteran status, or any other basis prohibited by law. If you are an individual with a disability and need assistance in applying for a position, please contact us at [email protected].
Know Your Rights:
https://www.eeoc.gov/sites/default/files/2022\-10/EEOC\_KnowYourRights\_screen\_reader\_10\_20\.pdf
Conozca sus Derechos:
https://www.eeoc.gov/sites/default/files/2022\-10/22\-088\_EEOC\_KnowYourRightsSp\_10\_20\.pdf
We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation at [email protected] .
Pay Range:
$177,920\.00 \- $266,880\.00 Annual
Incentive Compensation:
In addition to base pay, Zebra offers this role the opportunity to earn a performance\-based annual cash incentive, at a target equal to 25% of base pay, in accordance with the terms of the applicable incentive plan.
Zebra Total Rewards:
Zebra Total Rewards includes more than just pay and is structured to meet the needs of our changing global business and evolving talent. We are committed to providing our employees with a benefits program that is comprehensive and competitive – including healthcare, wellness, inclusion networks, and continued learning and development offerings. We offer community service days, in addition to the traditional insurances, compensation, parental leave, employee assistance program and paid time off offerings depending on the country where you work.
Salary offered will vary depending on your location, job\-related skills, knowledge, and experience.
Additionally, all Zebra roles are eligible for cash incentive programs. For example, sales roles have additional opportunity to earn substantial variable compensation tied to quota achievement. In most other roles, the Zebra annual cash incentive program links Company and individual performance together. Some roles may also be eligible for long\-term incentive equity awards.
Benefits:
We understand the importance of work\-life balance and wellbeing, which is why we offer flexibility for our teams including: hybrid work, adaptable hours, Summer Flex Fridays, Focus Fridays, and an annual companywide well\-being day to promote revitalization and success.
Job Posting Statement:
To protect candidates from falling victim to online fraudulent activity involving fake job postings and employment offers, please be aware our recruiters will always connect with you via @zebra.com email accounts. Applications are only accepted through our applicant tracking system and only accept personal identifying information through that system. Our Talent Acquisition team will not ask for you to provide personal identifying information via e\-mail or outside of the system. If you are a victim of identity theft contact your local police department.
AI Technology Statement:
Zebra Technologies leverages AI technology to evaluate job applications using objective, job\-relevant criteria. This approach enhances efficiency and promotes fairness in the hiring process. However, every decision regarding interviews and hiring is made by our dedicated team, because we believe people make the best decisions about people. For more on how we use technology in hiring and how we process applicant data, see our Zebra Privacy Policy .
Salary Context
This $177K-$266K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 2130 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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Zebra Technologies, 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 $185,000 based on 13,200 positions with disclosed compensation. Director-level AI roles across all categories have a median of $250,000. This role's midpoint ($222K) sits 20% above the category median. Disclosed range: $177K to $266K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Zebra Technologies AI Hiring
Zebra Technologies has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Holtsville, NY, US. Compensation range: $266K - $266K.
Location Context
Across all AI roles, 14% (583 positions) offer remote work, while 3,532 require on-site attendance. Top AI hiring metros: New York (2,760 roles, $211,000 median); San Francisco (2,258 roles, $253,000 median); Los Angeles (1,841 roles, $195,000 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, and the 90th percentile reaches $307,500. 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 $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.