Interested in this AI/ML Engineer role at Motorola Solutions?
Apply Now →About This Role
Company Overview
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At Motorola Solutions, we believe that everything starts with our people. We’re a global close\-knit community, united by the relentless pursuit to help keep people safer everywhere. We build and connect technologies to help protect people, property and places. Our solutions foster the collaboration that’s critical for safer communities, safer schools, safer hospitals, safer businesses, and ultimately, safer nations. Connect with a career that matters, and help us build a safer future.
Department Overview
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About Us:
At Motorola Solutions, we believe that exceptional customer and user experiences are born from a deep understanding of user needs, powered by insightful data, and brought to life through thoughtful design. Our team is a dynamic, multidisciplinary group, bringing together talented Industrial Designers, UX/CX Designers, User Researchers, and Human Factors practitioners. We are at the forefront of understanding our users and translating those insights into impactful products and services. Now, as we embrace the transformative potential of Artificial Intelligence, we're excited to expand our team with creative minds who can help us redefine what's possible.
The Opportunity: Integrate AI into World\-Class User Experiences
We are seeking talented and forward\-thinking AI\-Focused Experience Designers and to join our innovative team. This is a unique opportunity to work at the intersection of human\-centered design, cutting\-edge research methodologies, and artificial intelligence.
You won't just be working with AI; you'll be shaping how AI enhances and transforms the user experience. You'll collaborate closely with our existing experts in ID, CX, UX, and Research, as well as with data scientists and engineers, to envision, design, and evaluate AI\-driven solutions that are intuitive, ethical, and truly valuable to our users. Your work will directly contribute to our strategic goal of leading our industry through AI\-powered innovation.
Job Description
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What You'll Do (Core Responsibilities Adaptable by Role):
- Adapt to rapidly shifting priorities and contribute to an agile, fast\-paced innovation environment.
- Champion a user\-centered approach in the development and application of AI technologies.
- Demonstrate compelling storytelling skills as well as a command of design thinking principles and best practices
- Identify opportunities to leverage AI (ML, NLP, generative AI) to solve user problems and create novel, delightful experiences.
- Collaborate with product managers, engineers, and other stakeholders to translate AI capabilities into tangible user benefits and design requirements.
- Design and/or research user interactions with AI\-powered features and systems, ensuring they are intuitive, transparent, and trustworthy.
- Contribute to the development of best practices, ethical guidelines, and design patterns for Human\-AI interaction within our organization.
- Stay abreast of the latest advancements in AI and their potential applications in experience design and market fit.
- Effectively communicate findings, design concepts, and AI possibilities to diverse audiences.
- AI Literacy: A strong understanding of core Artificial Intelligence concepts, including Machine Learning (ML), Natural Language Processing (NLP), and Generative AI – their capabilities, limitations, and ethical considerations. You don't need to be an AI developer, but you must understand how these technologies work at a conceptual level.
- Problem Solver: Excellent analytical and critical thinking skills, with a knack for identifying user needs and framing them as design or research challenges.
- User\-Centric Mindset: A deep passion for understanding and advocating for the user.
- Collaboration \& Communication: Exceptional ability to collaborate effectively within a multidisciplinary team and articulate complex ideas clearly (verbally and visually).
- Adaptability \& Continuous Learning: Eagerness to learn new technologies and methodologies, particularly in the rapidly evolving AI space. Ability to thrive in an environment with dynamic priorities.
For AI\-Focused Experience Designer, you might specialize in:
- Human\-AI Interaction Design: Crafting intuitive and engaging interactions for AI\-powered solutions (e.g., conversational UIs, recommendation engines, generative content tools).
- End\-to\-End Design Process for AI\-Features: Experienced in the complete AI design lifecycle, from defining user needs to create prototypes and detailed specifications for AI features.
- AI\-Powered Prototyping \& Visualization: Using AI tools to rapidly prototype experiences or visualize complex AI\-driven data for users.
- Designing for AI Transparency \& Explainability: Creating interfaces that help users understand and trust AI\-driven decisions and outputs.
- Data\-Informed Design for AI: Proven ability to utilize user data, AI metrics, and research to design and improve AI outcomes.
- Ethical AI Design: Proactively identifying and mitigating bias and ethical concerns in the design of AI\-powered experiences.
- Speculative \& Future\-State Design: Exploring novel applications of emerging AI to envision future user experiences.
- Holistic AI Experience Evaluation: Assessing the overall AI user experience, including perceived intelligence, usefulness, reliability, and how errors, uncertainty, failures, and edge cases are managed and communicated.
- AI\-Augmented Methodologies: Leveraging AI tools for qualitative data analysis (e.g., thematic analysis of interviews, open\-ended surveys), sentiment analysis, or identifying patterns in large user datasets.
- Evaluating Experiences with AI\-Powered Features: Developing methods to assess the usability, utility, and implications of AI in user\-facing applications.
- Generative Synthesis with AI: Using AI to assist in synthesizing data to uncover insights and opportunities for AI application.
- AI and Customer Experience: Understanding AI\-driven opportunities to inform proactive CX strategies.
- 6\+ years proven experience in Design: (such as UX, CX, Interaction Design, Product Design) Portfolio: Showcasing exceptional UX design for apps and services that showcases your process, skills, and impact. Your portfolio should demonstrate a mastery of UX craft and the ability to drive complex projects. Demonstrable experience in AI assisted workflows and or multimodal interaction design is a plus.
\#LI\-JM2
Target Base Salary Range: $82,700 \- $109,400 USD
Consistent with Motorola Solutions values and applicable law, we provide the following information to promote pay transparency and equity. Pay within this range varies and depends on job\-related knowledge, skills, and experience. The actual offer will be based on the individual candidate.
Basic Requirements
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- Bachelor's or Master's degree: in HCI, Design, or a related field, or equivalent practical experience.
- 6\+ years proven experience in Design
Travel Requirements
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None
Relocation Provided
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None
Position Type
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Experienced
Referral Payment Plan
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Yes
Our U.S. Benefits include:
- Incentive Bonus Plans
- Medical, Dental, Vision benefits
- 401K with Company Match
- 10 Paid Holidays
- Generous Paid Time Off Packages
- Employee Stock Purchase Plan
- Paid Parental \& Family Leave
- and more!
*EEO Statement*
Motorola Solutions is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion or belief, sex, sexual orientation, gender identity, national origin, disability, veteran status or any other legally\-protected characteristic.
We are proud of our people\-first and community\-focused culture, empowering every Motorolan to be their most authentic self and to do their best work to deliver on the promise of a safer world. If you’d like to join our team but feel that you don’t quite meet all of the preferred skills, we’d still love to hear why you think you’d be a great addition to our team.
We’re committed to providing an inclusive and accessible recruiting experience for candidates with disabilities, or other physical or mental health conditions. To request an accommodation, please complete this Reasonable Accommodations Form so we can assist you.
Salary Context
This $82K-$109K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Motorola Solutions, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($96K) sits 55% below the category median. Disclosed range: $82K to $109K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Motorola Solutions AI Hiring
Motorola Solutions has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Chicago, IL, US, Plantation, FL, US. Compensation range: $109K - $133K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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.
Frequently Asked Questions
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