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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, a leading provider of advanced MANET and MIMO communications systems, is reshaping mesh network technology for mission\-critical applications – on the ground, in the air and at sea. Its battle\-proven StreamCaster family of MANET radios and proprietary MN\-MIMO waveform provides the vital communications link for defense, law enforcement and public safety agencies around the world, and in the toughest operational environments.
With deep roots in DARPA research, Silvus Technologies develops world\-class advanced communications technologies that are reshaping the tactical communications landscape. From pure line\-of\-sight to extreme non\-line\-of\-sight, Silvus radios form a self\-healing, self\-forming mesh network, enabling secure and reliable connectivity, including video and high\-bandwidth data.
Silvus Technologies is a wholly owned subsidiary of Motorola Solutions, Inc.
Job Description
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*Would you like to join an incredibly talented group of people, doing very challenging work, with the prime directive of “* *Keeping Our Heroes Connected* *”?*
THE OPPORTUNITY
Silvus is seeking a *Machine Learning Engineer* who will report to the *R\&D Director, Machine Learning* on the R\&D team. The successful individual in this role will focus on applying machine learning and data\-driven techniques to improve the performance, efficiency, and adaptability of Silvus’ advanced MIMO radios and wireless networking systems. This individual will work closely with experts in wireless communications, DSP, networking, and embedded systems to develop ML\-driven features that solve real\-world problems in dynamic and challenging RF environments.
This position is based at Silvus Technologies’ headquarters in the heart of vibrant West Los Angeles, CA, and is on a hybrid schedule. A minimum of 3 days onsite per week is expected. On\-site days are Mondays, Wednesdays, and Thursdays.
The following is a list of at least some of the current essential job functions of the position. Management may assign or reassign duties and responsibilities at any time at its discretion.
ROLE AND RESPONSIBILITIES
- Research, design, and implement machine learning algorithms to enhance performance in wireless communication systems (e.g., link adaptation, interference mitigation, anomaly detection, spectrum sensing).
- Analyze real\-world RF datasets to extract insights and develop predictive models.
- Develop software prototypes and integrate ML algorithms with Silvus’ radio firmware and networking stack.
- Collaborate with cross\-functional teams to define ML use cases and evaluate the impact of deployed models.
- Contribute to the design of data pipelines and infrastructure for training, testing, and validating models.
- Participate in performance benchmarking and iterative improvement cycles.
- Stay current with the latest Machine Learning research for wireless and embedded systems.
- Perform other related duties of which the above are representative.
REQUIRED QUALIFICATIONS
- Bachelor of Science degree in Electrical Engineering, Computer Science, Computer Engineering, or related field plus a minimum of 2 years of experience in machine learning, with demonstrated application to real\-world problems; no experience required with an advance degree (MS or PhD)
- Strong foundation in supervised and unsupervised learning and statistical modeling.
- Experience with Python ML frameworks (e.g., TensorFlow, PyTorch, scikit\-learn, etc.).
- Exposure to MATLAB or C/C\+\+ for signal processing algorithm development.
- Must be a U.S. Citizen due to clients under U.S. government contracts.
- All employment is contingent upon the successful clearance of a background check and drug test.
PREFERRED KNOWLEDGE, SKILLS, AND ABILITIES
- M.S. or Ph.D. in Electrical Engineering, Computer Science, or a related field.
- Demonstrated experience with RF signal classification, anomaly detection, or spectrum monitoring.
- Proficiency in MATLAB or C/C\+\+ for signal processing algorithm development.
- Familiarity with wireless communication concepts (e.g., PHY/MAC layers, MIMO, OFDM, spectrum access).
- Familiarity with embedded ML, real\-time systems, or deploying ML on edge devices.
- Background in adaptive modulation, beamforming, or cognitive radio techniques.
- Experience working with wireless standards such as 3GPP, IEEE 802\.11/15, or military waveforms.
- Experience with GPU acceleration or model optimization for constrained environments.
- Excellent communication and collaboration skills.
WORKING CONDITIONS AND PHYSICAL REQUIREMENTS
- Office environment.
- Outdoor environment for demos.
- Occasional exposure to heat, cold, and allergens while performing tests or demonstrations in the field.
- While performing the duties of this job, the employee is required to do the following:
- Lift equipment up to 20 lbs. for the set\-up of demonstrations and testing.
- Perform bending and reaching movements to place items on lower and higher shelves.
\#silvuscareers
COMPENSATION: $100,000 \- $140,000/annually
Basic Requirements
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- Bachelor of Science degree in Electrical Engineering, Computer Science, Computer Engineering, or related field plus a minimum of 2 years of experience in machine learning, with demonstrated application to real\-world problems; no experience required with an advance degree (MS or PhD)
- Strong foundation in supervised and unsupervised learning and statistical modeling.
- Experience with Python ML frameworks (e.g., TensorFlow, PyTorch, scikit\-learn, etc.).
- Exposure to MATLAB or C/C\+\+ for signal processing algorithm development.
- Must be a U.S. Citizen due to clients under U.S. government contracts.
Travel Requirements
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Under 10%
Relocation Provided
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Domestic
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 $100K-$140K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 2088 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 4,021 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 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 $180,000 based on 12,397 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $163,400. This role's midpoint ($120K) sits 33% below the category median. Disclosed range: $100K to $140K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($290,000) and AI Safety ($274,200). By seniority level: Entry: $97,760; Mid: $163,400; Senior: $227,400; Director: $244,800; VP: $250,000.
Motorola Solutions AI Hiring
Motorola Solutions has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span MA, US, Los Angeles, CA, US. Compensation range: $140K - $270K.
Location Context
AI roles in Los Angeles pay a median of $190,500 across 1,764 tracked positions. That's 5% 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 4,021 open positions tracked in our dataset. By seniority: 118 entry-level, 1,906 mid-level, 1,555 senior, and 442 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (608 positions). The remaining 3,392 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($290,000 median, 39 roles); AI Safety ($274,200 median, 52 roles); Research Engineer ($260,000 median, 421 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 4,021 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,818), Data Scientist (312), AI Software Engineer (280). 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 (118) are outnumbered by mid-level (1,906) and senior (1,555) 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 442 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (608 positions), with 3,392 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 $200,000. Top-quartile roles start at $253,000, 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 Engineering Manager roles lead at $290,000 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 (2,069 postings), Aws (1,260 postings), Azure (946 postings), Rag (893 postings), Gcp (783 postings), Pytorch (624 postings), Prompt Engineering (619 postings), Claude (570 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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