Machine Learning Engineer
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 OverviewDepartment Description
Our IT organization has a critical role in driving extraordinary business results. We’re looking for people who bring great ideas and who make our partners’ ideas better. Intellectually curious advisors (not order takers) who focus on outcomes to creatively solve business problems. People who not only embrace change but who accelerate it.
The Enterprise Architecture team has a multi-prong charter of being proactive to ensure the IT architecture matches our business vision, assuring the company can handle disruption, to ensure that our solutions and integrated designs match our infrastructure, and to give emerging technology full exploration and enable it for use within our company.
Job Description
Motorola Solutions is seeking a curious, motivated, and talented AI & Machine Learning Engineer to join our innovative AI Center of Excellence. This is an exciting opportunity to launch your career in AI/ML, working alongside experienced engineers to help build next-generation intelligent systems that make communities safer and more connected.
As a member of our AI COE team, you will:
Collaborate with senior engineers and researchers to apply Large Language Models (LLMs) and cutting-edge frameworks to real-world business challenges.
Contribute to the design and development of robust, scalable AI solutions and agents that deliver measurable impact.
Gain hands-on experience across the entire model lifecycle - from data preparation and training to deployment and monitoring.
Continuously learn and expand your technical skillset in a fast-paced, supportive environment with mentorship opportunities.
Requirements
Architect E2E Agentic Workflows: Design complex systems where AI agents collaborate to solve multi-step business problems.
Agent Orchestration: Implement robust orchestration layers using frameworks like LangGraph, AutoGen, or AgentSpace etc... Manage state, memory, and sequential handoffs between agents.
Hybrid Agent Strategy: Seamlessly integrate "Out-of-the-Box" Agents (e.g., Domain Specific Agents, Salesforce Agentforce) for standard tasks with highly specialized Custom Agents built on proprietary data.
Glass-Box Observability: Engineer "Glass Box" transparency. Implement deep tracing and clear logging to visualize the "Chain of Thought," ensuring we understand why an agent made a decision, not just what it decided.
Tool Use & Function Calling: Enable agents to interact with external APIs, databases, and internal tools to execute actions autonomously.
Core Modeling: Design and deploy standard algorithms for Regression, Classification, Forecasting, and Clustering.
Feature Engineering: Perform deep exploratory data analysis (EDA) and feature selection to determine which variables drive business value.
Statistical Rigor: Display and interpret standard data science statistics (p-values, confidence intervals, error analysis) to validate model reliability before production.
Continuous Optimization: Regularly rebuild and retrain production models to combat drift and improve accuracy/efficiency.
LLM Implementation: Research and implement practical applications using Large Language Models (LLMs), focusing on RAG (Retrieval-Augmented Generation) and fine-tuning.
Risk Mitigation: Actively monitor and mitigate risks associated with GenAI, including hallucination, bias, and data privacy vulnerabilities.
Evangelism: Translate complex AI concepts into accessible language for stakeholders. Prototype new AI services quickly to showcase capabilities to customers.
Production Deployment: Containerize models using Docker and orchestrate them via Kubernetes.
API Development: Build robust APIs (FastAPI/Flask) to serve predictions and agent responses to integration teams.
Standards & Procedures: Develop coding standards and CI/CD procedures for the Machine Learning platform.
Performance Tuning: Optimize models (quantization, caching) for latency and cost efficiency.
Basic Requirements
4+ years of software engineering experience with a track record of delivering AI/ML models to production.
Strong "Hybrid" Experience: You have professional experience in both Traditional ML (Scikit-learn/Pandas) and modern GenAI/LLM development.
Agentic Framework Knowledge: Hands-on experience exploring or building with Agent orchestration tools (LangGraph, AutoGen, etc.).
Observability Mindset: Proven ability to implement logging and tracing for complex, non-deterministic workflows.
Containerization: Expertise with Docker and Kubernetes for deploying scalable AI solutions.
Communication: Ability to explain complex algorithms to non-technical business partners.
Curiosity: A self-starter who actively keeps up with the rapid changes in the AI landscape (e.g., new model releases, new agent patterns).
Travel RequirementsNone
Relocation ProvidedNone
Position TypeExperienced
Referral Payment Plan
NoEEO 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.