Hybrid Deep Learning Engineer II — Multimodal AI & Signal Processing

Robert Bosch Group; Pittsburgh (Allegheny County); Hybrid; USD 110,000 - 160,000; Full time

Job summary

Robert Bosch Group in Pittsburgh seeks a Deep Learning Engineer II to conduct R&D of signal processing and AI systems on embedded platforms. You will work on multimodal data (audio, vibration, radar, lidar) and develop algorithms and AI components integrated with large language models to automate complex tasks.

Requires a master’s degree and 3 years of relevant experience in deep learning pipelines; proficiency with Python, PyTorch, MLFlow, NumPy, SciPy, and Matplotlib; hybrid work schedule with

Qualifications

• Master’s degree or foreign equivalent in Information Technology, Electronic Engineering or a related field.
• 3 years of experience as a Deep Learning Engineer or related role.
• Experience with deep learning and signal processing frameworks in Python, PyTorch, Lightning, MLFlow, NumPy, SciPy, Matplotlib.

Responsibilities

• Conduct research and advanced development of signal processing and deep learning systems for diverse sensor data (audio, vibration, ultrasounds, radar, lidar, power traces, torque).
• Develop AI systems integrated with large language models for agentic workflows and automation.
• Create multimodal retrieval-augmented generation applications combining data types to generate contextually relevant responses.
• Collaborate with global teams to deploy AI solutions on embedded platforms.

Skills

Deep learning

Signal processing

Python

Education

Master’s degree

Tools

PyTorch

Lightning

MLFlow

NumPy

SciPy

Matplotlib

Python

Job description

Robert Bosch Group in Pittsburgh seeks a Deep Learning Engineer II to conduct R&D of signal processing and AI systems on embedded platforms. You will work on multimodal data (audio, vibration, radar, lidar) and develop algorithms and AI components integrated with large language models to automate complex tasks.

Requires a master’s degree and 3 years of relevant experience in deep learning pipelines; proficiency with Python, PyTorch, MLFlow, NumPy, SciPy, and Matplotlib; hybrid work schedule with