Interested in this AI/ML Engineer role at Motorola Solutions?
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Company Overview
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
Motorola Solutions' innovations, products, and services play essential roles in people's lives. Our end-to-end suite of software solutions helps customers manage emergency communications, process video and evidence, and leverage cutting-edge AI-driven analytics for security and operational insights. We are industry leaders in video security and analytics, with solutions deployed in more than 120 countries across diverse environments such as school campuses, transportation systems, healthcare centers, public venues, critical infrastructure, prisons, factories, casinos, airports, financial institutions, government facilities, and retailers.
Our AI-powered security solutions integrate advanced video analytics, machine learning, and embedded intelligence to enable proactive threat detection, enhanced situational awareness, and automated decision-making. Job Description
Reporting to the VP of AI for Motorola Solutions, the Principal Technical Program Manager- Voice AI will be a critical, high-impact leader who thrives on turning highly ambiguous concepts into enterprise-scale AI products and solutions. They will be responsible for driving the strategy and measurable success of our core Artificial Intelligence efforts. This role is pivotal in managing the lifecycle and adoption of central AI models for speech and language understanding (ASR, TTS, translation, LLMs and others) and the foundational AI infrastructure built by our organization and consumed by downstream product teams for their features. This includes building enterprise-scale cloud and edge AI platforms, ensuring that our AI investments translate into quantifiable, improved customer outcomes.
Key Responsibilities
The Principal TPM of Voice AI will own the strategy, definition, and execution of programs across the following core areas:
- End-to-End AI Feature Delivery: Drive and coordinate efforts across the central AI organization and all dependent product teams to ensure the seamless, end-to-end delivery of AI features in the area of Voice AI such as building voice and language understanding based models and services and agents for products that demonstrably deliver value for our end customers.
- AI Impact Tracker & Baseline: Work across various product teams to define and implement ways to establish and measure a systemic link AI model performance and customer outcomes. This includes baselining current customer impact and owning the program to drive measurable, continuous improvement in AI effectiveness.
- AI for Developer Practices: Drive strategic programs to integrate AI/ML into our internal software development lifecycle, tracking adoption, evangelizing best practices, and reporting on the impact of these tools on engineering velocity and quality.
- Organizational Performance Management: Design, implement, and maintain a high-value, executive-facing dashboard that tracks all key AI impact metrics, development efficiency metrics, and overall organizational goals. Managing the data fidelity and reporting accuracy is a core responsibility.
- Cross-Functional Alignment & Impact Communication: Own the organizational mechanism for driving transparency and alignment with partner product teams. This includes regularly presenting the status of core AI metrics, communicating strategic research initiatives to move those metrics, tracking progress, and quantifying the actual impact delivered to stakeholders and customers.
- Emergent Strategic Initiatives: Provide immediate program management and definition for critical projects. This is a dynamic responsibility that may involve:
- Bringing initial definition to highly ambiguous product concepts.
- Conducting early-stage customer/partner discovery.
Ensuring these initiatives are properly prioritized, staffed, and smoothly transitioned to a dedicated Product Manager (PdM) once scoped. *
Qualifications & Experience
- 10+ years of technical program management experience, with significant time spent leading complex, cross-functional programs in AI/ML research and platforms.
- Technical Acumen: Deep understanding of AI and Machine learning, preferably speech and models and LLMs, Machine Learning lifecycle (MLOps), data platforms, and the challenges of building and deploying central, reusable AI infrastructure and models. A background in computer vision and/or voice technologies is a significant plus..
- Ability to engage in technical discussions with engineering teams, understand key concepts and provide guidance and support
- Metrics Expertise: Proven experience defining and implementing metrics frameworks (KPIs, OKRs) to measure the success of core technology platforms and drive organizational change and accountability.
- Strong analytical skills, with experience working with AI performance metrics (e.g. Precision/Recall), real-time video processing, and inference optimization.
- Leadership: Demonstrated ability to operate at a Principal level, influence senior leaders across organizations, drive consensus, and communicate complex technical and strategic concepts clearly and concisely.
- Excellent oral and written communication skills, with the ability to present ideas, roadmap elements, and business cases to both technical and non-technical stakeholders.
- Customer-focused with a track record of building strong relationships and understanding customer needs
- Domain Knowledge: Experience in public safety, security-focused software (e.g., video security, evidence management), mission-critical systems, or emergency communications is a plus.
- Pathfinding: Experience bringing initial definition to "highly ambiguous" product concepts and conducting early-stage discovery for emergent initiatives.
Target Base Salary Range: 180,000 - 220,000 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.
Note: Candidates must reside in a US state within the Eastern Time Zone (ET).
#LI-MP2
#LI-REMOTE
Basic Requirements
- 10+ years of technical program management experience
- AND 3 + years of experience of hands on work as an individual contributor building models, ML pipelines or developing core speech algorithms or systems in speech or other domains or working as a software engineer in a data oriented product.
- AND 3+ years of hands-on experience as an individual contributor in machine learning modeling, production ML pipelines, speech processing algorithms, or software engineering for data-oriented products.
Travel Requirements
Under 10% Relocation Provided
None Position Type
Experienced Referral Payment Plan
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 $180K-$220K 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 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 $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 ($200K) sits 6% below the category median. Disclosed range: $180K to $220K.
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.
Motorola Solutions AI Hiring
Motorola Solutions has 6 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager. Positions span Waltham, MA, US, Chicago, IL, US, Waltham, MA. Compensation range: $125K - $220K.
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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