Senior Edge AI Engineer - Embedded ML for Audio

TetraMem - Accelerate The World; San Jose (CA); Sur place; USD 110 000 - 200 000; Plein temps

Résumé du poste

TetraMem - Accelerate The World is seeking a senior machine learning engineer to design, optimize, and deploy lightweight edge AI models for audio processing on embedded hardware. You will work closely with hardware teams to ensure seamless integration on FPGA and custom ASIC solutions in a fast-paced startup environment.

You will lead ML model optimization efforts, exploring quantization, pruning, and distillation, and mentor junior engineers while publishing findings and contributing to open

Qualifications

• 5+ years of experience or PhD in Computer Science, Electrical Engineering, or related fields.
• Strong experience in machine learning, with a focus on edge AI and lightweight model deployment.
• Expertise in ML frameworks such as PyTorch, TensorFlow, JAX.
• Proficiency in C/C++, Python and ML model optimization.
• Ability to mentor junior engineers and work in a fast-paced startup environment.

Responsabilités

• Develop, optimize, and deploy lightweight ML models for edge AI applications, especially audio processing.
• Implement and optimize ML models on embedded platforms, including FPGA and ASIC solutions.
• Collaborate with hardware and software teams to productionize ML models.
• Research and implement state-of-the-art ML techniques to improve efficiency, latency, and power.
• Improve inference efficiency and model compression (quantization, pruning, distillation).
• Drive innovation and contribute to system architecture.
• Provide technical leadership and mentorship to junior engineers.
• Publish research findings and contribute to open-source projects.

Connaissances

Edge AI

ML frameworks (PyTorch)

ML frameworks (TensorFlow)

ML frameworks (JAX)

C/C++

Python

Mentorship

Formation

PhD in CS/EE

Outils

PyTorch

TensorFlow

JAX

Description du poste

TetraMem - Accelerate The World is seeking a senior machine learning engineer to design, optimize, and deploy lightweight edge AI models for audio processing on embedded hardware. You will work closely with hardware teams to ensure seamless integration on FPGA and custom ASIC solutions in a fast-paced startup environment.

You will lead ML model optimization efforts, exploring quantization, pruning, and distillation, and mentor junior engineers while publishing findings and contributing to open