Mid-Level Machine Learning Engineer

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 programming languages such as C/C++, Python, and experience with ML model optimization
• Ability to work independently and collaboratively in a fast-paced startup environment
• 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

Benefits

• Salary Range: $110,000 - $300,000 / year

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

Full Description

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 toenhance 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 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 programming languages such as C/C++, Python, and experience with ML model optimization.

• Ability to work independently and collaboratively in a fast-paced startup environment.

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: $110,000 - $300,000 / year