XR Multimodal AI Engineer — On-Device LLMs & Agents

Google; San Jose (CA); In loco; USD 147.000 - 210.000; Tempo pieno

Descrizione del lavoro

Google is seeking a software engineer focused on advancing multimodal and conversational AI for next-gen wearable XR devices. You will develop and optimize on-device and hybrid multimodal models for glasses and headsets, leveraging prompting, few-shot learning, and post-training techniques while managing power and latency constraints.

Requirements include a strong background in ML, LLMs, and experience with JAX/TensorFlow/PyTorch.

Competenze

• Bachelor's degree or equivalent practical experience required.
• 2 years of software development in one or more languages, or 1 year with an advanced degree.
• 1 year in speech/audio, reinforcement learning, ML infrastructure, or related ML field.
• 1 year experience with ML frameworks such as JAX, TensorFlow, or PyTorch.

Mansioni

• Lead development and optimization of on-device and hybrid multimodal models for XR devices.
• Create evaluation plans for hybrid systems and define KPIs for model accuracy and on-device efficiency.
• Identify, implement, and ship latest modeling innovations for hybrid agent architectures and edge-cloud orchestration.
• Prove concepts for on-device AI features through rapid prototyping and collaboration with XR product teams.
• Collaborate with research scientists, engineers, and product teams to drive the technical roadmap and contribute to publications and launches.

Conoscenze

Software development

Speech/Audio ML

Reinforcement learning

ML infrastructure

Multimodal ML

Formazione

Bachelor's degree or equivalent

Master's or PhD in CS or related

Strumenti

JAX

TensorFlow

PyTorch

Descrizione del lavoro

Google is seeking a software engineer focused on advancing multimodal and conversational AI for next-gen wearable XR devices. You will develop and optimize on-device and hybrid multimodal models for glasses and headsets, leveraging prompting, few-shot learning, and post-training techniques while managing power and latency constraints.

Requirements include a strong background in ML, LLMs, and experience with JAX/TensorFlow/PyTorch.