Interested in this AI/ML Engineer role at Motorola Solutions?
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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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Silvus Technologies is dedicated to one mission: connecting those who keep us safe. We do so by delivering the most advanced Mobile Ad\-hoc Network (MANET) radios powered by our custom and ever evolving Mobile\-Networked MIMO waveform. Together, our radios and waveform provide vital communications for mission\-critical applications in the harshest environments from underground tunnels to high\-altitude balloons.
Silvus StreamCaster™ radios are being rapidly adopted by customers all over the world ranging from the U.S. and Allied Nations Departments of Defense to International, Federal, State, and Local Law Enforcement agencies, all the way to the Superbowl, Grammys, and industry\-leading drone, robot, and unmanned systems manufacturers.
We’re excited about the work we are doing in AI. Founded on our many years of experience and knowledge, we know the mission\-critical needs of our customers are unique and different from the consumer technologies that leverage AI today.
Silvus Technologies is a wholly owned subsidiary of Motorola Solutions, Inc.
Job Description
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Would you like to join a talented group of people, doing challenging work, with the mission of “Keeping Our Heroes Connected”?
You will join the Signals and Systems group where you will be developing machine learning solutions that enhance performance in wireless communication systems. Your work will directly impact our customers in the form of products and services that make use of signal processing technology. Unlike similar roles in the industry, the result of your work has the potential to impact the entire lifecycle of the workflows impacting people's lives in moments that matter.
Roles and Responsibilities
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- Manage inputs gathered from unusual sources, including captures from software defined radio (SDR) over a wide range of RF signals
- Combine knowledge of signal processing, probability and statistics, machine learning, and modern methods of artificial intelligence to build large\-scale and high\-throughput systems handling vast quantities of data
- Collaborate with UX designers, infrastructure engineers, and other research scientists to develop prototypes and integrate ML algorithms that work across a wide range of scales from resource\-constrained edge compute to full\-sized data centers
- Stay current with the latest machine learning research for wireless and embedded systems, applying ingenuity and a deep understanding of the problems at hand
Required Skills
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- 6\+ years experience as a machine learning engineer
- Expert knowledge in Python and PyTorch or TensorFlow
- Strong foundation in supervised and unsupervised learning and statistical modeling
- Strong mathematics background, particularly in linear algebra and probability
- Strong written and oral communication skills
Desired Skills
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- Advanced degree in a quantitative field such as electrical and computer engineering, physics, mathematics or statistics
- Familiarity with relational and NoSQL databases
- Familiarity with RF signal processing and SDR for signals intelligence or electronic warfare
- Familiarity with cloud\-based infrastructure: Azure and/or AWS
- Experience tracking projects with Jira, Azure DevOps or similar tooling
- Experience with Linux, DevOps (command line)
- Experience with containerized infrastructure (Docker, Kubernetes)
- Familiarity with regulated environments, such as sovereign clouds
Target Base Salary Range: $195,000 USD \- $225,000 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.
*As a part of the application process, we encourage you to include a link to code you’ve written.*
*Note: This role is 100% on\-site in our Los Angeles office.*
\#LI\-MP2
\#LI\-ONSITE
Basic Requirements
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- Bachelor degree with 8\+ years experience as a machine learning engineer
- AND 6\+ years of Python and PyTorch or TensorFlow experience
- Must be a U.S. citizen with the ability to obtain necessary security clearance as required by government contract.
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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No
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 $195K-$225K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($210K) sits 16% above the category median. Disclosed range: $195K to $225K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Motorola Solutions AI Hiring
Motorola Solutions has 9 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer, AI Software Engineer. Positions span Waltham, MA, US, MA, US, Los Angeles, CA, US. Compensation range: $155K - $290K.
Location Context
AI roles in Los Angeles pay a median of $191,580 across 1,792 tracked positions. That's 4% below the national 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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