Senior Machine Learning Engineer

TetraMem - Accelerate The World; San Jose (CA); On-site; USD 200,000 - 280,000; Full time

Job summary

TetraMem - Accelerate The World is hiring a senior ML engineer in San Jose, CA to develop, optimize, and deploy lightweight edge AI models for audio processing on embedded platforms, including FPGA and ASIC solutions.

You will lead ML initiatives, collaborate with hardware and software teams, and explore state-of-the-art techniques to improve efficiency, latency, and power consumption for embedded AI applications, while driving model compression techniques and contributing to open-source

Qualifications

• 5+ years of relevant industry experience (or a PhD) in Computer Science, Electrical Engineering, Machine Learning, or related fields.
• Must have prior experience managing a team, serving in a Team Lead role, or demonstrating strong technical leadership and cross-functional coordination capabilities.
• Strong hands-on experience in machine learning, with a focus on edge AI, on-device inference, and deploying lightweight models on resource-constrained devices.
• Proficiency in Python and C/C++, with practical experience in ML model optimization and production deployment.
• Deep experience with model quantization (PTQ/QAT), pruning, knowledge distillation, sparsity, and other compression techniques for efficient edge inference.
• Hands-on experience developing for or integrating with AI chip SDKs, neural accelerators (NPUs/DSPs), or hardware-specific toolchains (e.g., NVIDIA TensorRT, Qualcomm Neural Processing SDK, ARM Ethos, or similar).
• Familiarity with edge inference runtimes (ONNX Runtime, ExecuTorch, TVM) and optimizing models for hardware constraints (latency, memory footprint, power consumption).

Responsibilities

• Develop, optimize, and deploy lightweight machine learning models for edge AI applications, particularly for audio processing.
• Implement and optimize ML models on embedded platforms, including FPGA and custom ASIC solutions.
• Work closely with hardware and software teams to integrate ML models into production systems.
• Research and implement state-of-the-art ML techniques to enhance model efficiency, latency, and power consumption for embedded AI applications.
• Improve inference efficiency and model compression techniques, including quantization, pruning, and knowledge distillation.
• Collaborate with cross-functional teams to drive innovation and contribute to the overall system architecture.
• Provide technical leadership and mentorship to junior engineers.
• Publish research findings, present at conferences, and contribute to open-source projects when applicable.

Skills

Edge AI

On-device inference

Python

C/C++

Model optimization

Team leadership

Cross-functional collaboration

Knowledge distillation

Quantization

Pruning

Education

PhD

Tools

PyTorch

TensorFlow

TensorFlow Lite

JAX

ONNX Runtime

TVM

TensorRT

NVIDIA TensorRT

Qualcomm Neural Processing SDK

ARM Ethos

Job description

• Develop, optimize, and deploy lightweight machine learning models for edge AI applications, particularly for audio processing.


• Implement and optimize ML models on embedded platforms, including FPGA and custom ASIC solutions.


• Work closely with hardware and software teams to integrate ML models into production systems.


• Research and implement state-of-the-art ML techniques to enhance model efficiency, latency, and power consumption for embedded AI applications.


• Improve inference efficiency and model compression techniques, including quantization, pruning, and knowledge distillation.


• Collaborate with cross-functional teams to drive innovation and contribute to the overall system architecture.


• Provide technical leadership and mentorship to junior engineers.


• Publish research findings, present at conferences, and contribute to open-source projects when applicable.

Responsibilities

• Develop, optimize, and deploy lightweight machine learning models for edge AI applications, particularly for audio processing.


• Implement and optimize ML models on embedded platforms, including FPGA and custom ASIC solutions.


• Work closely with hardware and software teams to integrate ML models into production systems.


• Research and implement state-of-the-art ML techniques to enhance model efficiency, latency, and power consumption for embedded AI applications.


• Improve inference efficiency and model compression techniques, including quantization, pruning, and knowledge distillation.


• Collaborate with cross-functional teams to drive innovation and contribute to the overall system architecture.


• Provide technical leadership and mentorship to junior engineers.


• Publish research findings, present at conferences, and contribute to open-source projects when applicable.

Requirements

• 5+ years of relevant industry experience (or a PhD) in Computer Science, Electrical Engineering, Machine Learning, or related fields.


• Must have prior experience managing a team, serving in a Team Lead role, or demonstrating strong technical leadership and cross-functional coordination capabilities.


• Strong hands-on experience in machine learning, with a focus on edge AI, on-device inference, and deploying lightweight models on resource-constrained devices.


• Expertise in modern ML frameworks such as PyTorch, TensorFlow (including TensorFlow Lite), and JAX.


• Proficiency in Python and C/C++, with practical experience in ML model optimization and production deployment.


• Deep experience with model quantization (PTQ/QAT), pruning, knowledge distillation, sparsity, and other compression techniques for efficient edge inference.


• Hands-on experience developing for or integrating with AI chip SDKs, neural accelerators (NPUs/DSPs), or hardware-specific toolchains (e.g., NVIDIA TensorRT, Qualcomm Neural Processing SDK, ARM Ethos, or similar).


• Familiarity with edge inference runtimes (ONNX Runtime, ExecuTorch, TVM) and optimizing models for hardware constraints (latency, memory footprint, power consumption).

Experience in one or more of the following areas considered a strong plus:

• Understanding of ML compiler and runtime design.


• Experience working with tools such as Optimum, ONNX, TensorRT, TFLite/LiteRT, ncnn, or CoreML.


• Familiarity with hardware acceleration techniques.


• Experience in embedded system development.

Salary Range:

$200,000 - $280,000 / year

TetraMem celebrates diversity and is committed to creating an inclusive environment for all employees. We are proud to be an Equal Opportunity Employer and welcome applicants from all backgrounds. Qualified candidates will receive consideration for employment without regard to race, color, religion, creed, sex, gender identity or expression, sexual orientation, national origin, ancestry, age, marital status, medical condition, disability, genetic information, military or veteran status, or any other characteristic protected by applicable federal, state, or local law.

TetraMem is committed to providing reasonable accommodations to qualified applicants with disabilities throughout the recruitment process. Applicants requiring accommodation may contact Human Resources for assistance.