Data Science Engineer

7 giorni fa

Firenze, Provincia di Firenze; Toscana, Italia Verizon Tempo pieno

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)