Senior ML Engineer - Edge AI & Hardware Optimization

Nutanix; San Diego (CA); On-site; USD 123,000 - 184,000; Full time

Benefits offered by this job

Competitive benefits

RSU grants

Job summary

Qualcomm Technologies, Inc. seeks a Machine Learning Engineer to create and implement ML techniques, frameworks, and tools for efficient discovery and use of state-of-the-art ML solutions across multiple verticals.

The role involves collaborating with cross-functional teams to enhance mobile, edge, auto, and IoT products through ML-enabled hardware and software. Strong background in ML frameworks and low-level OS interactions is required.

Qualifications

• Bachelor's degree in Computer Science, Engineering, Information Systems, or related field and 6+ years of Hardware/Software/Systems Engineering experience.
• Master's degree in Computer Science, Engineering, Information Systems, or related field and 5+ years of Hardware/Software/Systems Engineering experience.
• PhD in Computer Science, Engineering, Information Systems, or related field and 4+ years of Hardware/Software/Systems Engineering experience.

Responsibilities

• Leverages advanced Machine Learning knowledge to extend training or runtime frameworks or model efficiency software tools with new features and optimizations.
• Models, architectures, and develops highly advanced machine learning hardware (co-designed with ML software) for inference or training solutions.
• Develops optimized software to enable AI models deployed on hardware (e.g., kernels, compiler tools, model efficiency tools) and collaborates with hardware teams.

Skills

Machine Learning

Software Engineering

Hardware Integration

Education

Bachelor's degree

Master's degree

PhD

Tools

TensorFlow

Caffe

PyTorch

Keras

Linux

Job description

Qualcomm Technologies, Inc. seeks a Machine Learning Engineer to create and implement ML techniques, frameworks, and tools for efficient discovery and use of state-of-the-art ML solutions across multiple verticals.

The role involves collaborating with cross-functional teams to enhance mobile, edge, auto, and IoT products through ML-enabled hardware and software. Strong background in ML frameworks and low-level OS interactions is required.