Edge ML Engineer: Real-Time Inference & System Optimization

Zoox; Foster City (CA); On-site; USD 100,000 - 140,000; Full time

Job summary

Zoox in Foster City, California, is seeking a Machine Learning and System Optimization Engineer to innovate in developing multi-modality models for autonomous systems. You will manage system capacities and drive initiatives for efficient model inference.

The successful candidate will optimize and deploy complex models, focusing on real-time execution on edge devices. Key skills include model compression, CUDA programming, and experience with power-constrained vehicle systems.

Qualifications

• Hands-on experience with compressing and deploying complex models.
• Experience with large-scale models for constrained environments.
• Ability to write efficient CUDA code and optimize ML models.

Responsibilities

• Orchestrate system capacity for core perception models.
• Drive initiatives for efficient inference across perception stacks.
• Optimize machine learning models for real-time execution.

Skills

Model compression

CUDA programming

Machine learning optimization

Concurrent programming

Job description

Zoox in Foster City, California, is seeking a Machine Learning and System Optimization Engineer to innovate in developing multi-modality models for autonomous systems. You will manage system capacities and drive initiatives for efficient model inference.

The successful candidate will optimize and deploy complex models, focusing on real-time execution on edge devices. Key skills include model compression, CUDA programming, and experience with power-constrained vehicle systems.