Data Science Engineer
Pubblicato il 03-10-2026 - Verizon in Firenze
Overview
Join Verizon’s AI team to design, train, and optimize ML and CV models powering edge devices and cloud backends. You will collaborate with cross-functional teams to deliver reliable, high-performance AI solutions that improve device health, usage insight, and user safety. This role blends research, practical deployment, and production-ready software to impact a global product ecosystem. You’ll work in Florence with a hybrid schedule and have opportunities to shape innovative AI applications at scale.
Retribuzione / Benefits
- competitive pay and performance-based incentives
- health and wellbeing resources
- Employee Assistance Program
- paid time off
- flexible working arrangements
- training and development funding
Responsabilità
- Design, train, and evaluate ML, computer vision, and deep learning models for edge devices and cloud backends
- Curate large-scale labeled, pseudolabeled, or synthetic datasets with quality and privacy compliance
- Benchmark and optimize models for memory, latency, and power constraints
- Write high-quality, production-ready code with TDD, CI/CD, and solid software design
- Collaborate with embedded software, firmware, product, and hardware teams to integrate models into devices and pipelines
- Support model deployment and establish monitoring workflows for health and drift in production
- Conduct research, build POCs on emerging AI/CV technologies, and contribute to innovation goals
Requisiti fondamentali
- Master in Computer Science, Software Engineering or equivalent
- Experience with Python and ML/DL libraries (PyTorch, TensorFlow, NumPy, Pandas)
- Hands-on CV/ML model development and evaluation on real-world datasets
- Familiarity with Git, unit testing, and modular code design
- Experience using AI coding assistants
- Knowledge of Agile development workflows
- excellent communication
- cross-functional collaboration
- problem solving and adaptability
- edge/embedded model optimization (ONNX, TensorRT, TFLite, quantization/pruning)
- production integration with C/C++ or Rust
- multimodal data processing (e.g., CV with time-series data like IMU, GPS, telematics)