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Location
In office (Malaysia)
Seniority
Staff · 5+ years exp
Employment
Full-Time
Overview
Company
Impact
Profile match
Motorola Solutions is a global leader in public safety and enterprise security solutions, specializing in land mobile radio communications, video security, access control, and command center software. Created in 2011 following the spin-off of Motorola's mobile phone division, the Chicago-based company primarily serves law enforcement, emergency medical services, government agencies, and commercial enterprises. Its integrated ecosystem connects critical communications, AI-powered video analytics, and emergency dispatch systems to help organizations safeguard communities and operations.

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

We engineer the future of safety and security.

Our connected technologies enable seamless, mission-critical communications for public safety and enterprise organizations, protecting people, property, and communities.

Job Description

Core Responsibilities

1. Strategic AI Research & Risk Assessment

  • Continuously scout the AI landscape for mature, stable technologies (e.g., classical ML, proven GenAI, robust audio processing) that are ready for enterprise deployment.

  • Conduct rigorous risk assessments (security, bias, latency, hallucination risks) to ensure proposed AI solutions are safe for mission-critical communication environments.

  • Develop proof-of-concepts (PoCs) to validate the performance, cost-to-serve, and operational impact of new AI tools before wide-scale rollout.

2. Operational & Process Optimization

  • Identify bottlenecks in development, QA, and deployment pipelines, deploying AI tools (e.g., AI-assisted code reviews, automated test generation) to enhance engineering productivity.

  • Implement AI-driven telemetry and predictive maintenance for cloud infrastructure to proactively detect outages and reduce compute costs.

  • Streamline customer support and onboarding processes using secure, RAG-based (Retrieval-Augmented Generation) knowledge assistants for dispatchers and IT admins.

3. Product Enhancement (Communications in & Cloud Solutions)

  • Audio & Speech AI:Propose and integrate edge-to-cloud audio models. Focus on advanced audio features, i.e. noise suppressors, real-time speech-to-text in high-noise environments, and acoustic anomaly detection.

  • Dispatch & Cloud Intelligence:Design AI integrations for cloud dispatch systems, such as automated radio chatter summarization, predictive location-based resource allocation, and smart keyword alerting.

  • Performance Optimization:Leverage predictive AI to optimize cloud routing, reduce latency in PTT call setup, and manage bandwidth dynamically over LTE/Wi-Fi networks.

Basic Requirements

Experience & Mindset

  • Experience:5+ years in AI/ML engineering, product management, or technical strategy, ideally within cloud SaaS, telecommunications, or unified communications (UCaaS).

  • Risk-Aware Innovation:Demonstrated ability to distinguish between "hype" and "stable, production-ready" AI. Experience deploying AI in zero-fault tolerance or high-reliability environments.

  • Cross-Functional Leadership:Proven track record of translating complex AI concepts into business value (ROI, cost-savings, performance metrics) for stakeholders.

Technical Competencies

  • Audio/Voice ML:Familiarity with audio processing models, speech recognition (ASR), and natural language processing (NLP) tailored for real-time communication.

  • Cloud AI Services:Deep understanding of managed AI services across major cloud providers (AWS, Azure, GCP) and how to architect them for cost-efficiency.

  • Edge vs. Cloud Architecture:Strong understanding of when to process AI workloads on the edge (e.g., on rugged PTT devices) versus in the cloud to balance latency, battery life, and compute costs.

  • MLOps:Knowledge of model deployment, monitoring, and lifecycle management to ensure AI features do not degrade over time.

Travel Requirements

None

Relocation Provided

None

Position Type

Experienced

Referral Payment Plan

Yes

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.

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