Core Responsibilities
As a Senior Machine Learning Engineer, you will contribute heavily to core algorithm development while leveraging your specific strengths:
Core AI/ML Development
Design, train, and fine-tune high-performance deep learning models for complex vehicle attribute detection and optical character recognition (OCR) utilizing modern architectures (CNNs, Vision Transformers).
Apply a Data-Centric AI approach, potentially leveraging Generative AI (Diffusion Models, GANs) for Synthetic Data Generation to overcome long-tail anomalies and obscured data in natural environments.
Write clean, highly efficient, well-documented, and maintainable production-level code (Python/C++).
Specialized Domain Focus (Depending on your primary expertise)
If specializing in Edge AI: Rigorously profile trained models to identify latency bottlenecks. Apply advanced optimization techniques (INT8/FP16 quantization, pruning) and convert models for deployment via TensorRT, TFLite, or ONNX on embedded hardware (ARM CPUs, custom NPUs, low-power devices).
If specializing in Cloud AI & Data Mining: Architect and develop advanced machine learning algorithms and AI pipelines to mine actionable insights from massive cloud-based datasets. Focus on large-scale model training, integrating Vision-Language Models (VLMs) for complex text-to-image retrieval, and optimizing deep learning architectures to process and extract intelligence from high volumes of unstructured video/image data.
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Basic Requirements
Experience & Education
Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Computer Vision, or a related technical field.
7+ years of rigorous professional experience in applied Machine Learning, Computer Vision, or AI Research/Development.
Proven hands-on track record of transitioning models from research/prototypes into robust commercial production.
Technical Competencies
Deep practical mastery of AI frameworks, primarily PyTorch or TensorFlow.
Strong programming proficiency in modern Python and/or C++ within Linux environments.
Specialization Flexibility: Demonstrated deep expertise and practical achievement in AT LEAST ONE of the following domains:
Domain A (Edge AI): Hardware acceleration, edge model profiling, model compression, and deployment tools (TensorRT, TFLite, edge devices).
Domain B (Cloud AI & Data Mining): Deep expertise in large-scale AI model training, computer vision algorithms, and designing complex AI pipelines for handling massive datasets (e.g., extracting statistical patterns and intelligence from billions of records).
Preferred
Familiarity with Multimodal Foundation Models (e.g., CLIP, LLaVA) for Open-Set Recognition or Text-to-Image retrieval.
Experience in building Agentic workflows or automated Active Learning systems.
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.
