Edge AI Systems Engineer — Embedded ML & FPGA/ASIC

TetraMem INC; Wayne (CA); In loco; USD 110.000 - 250.000; Tempo pieno

Descrizione del lavoro

TetraMem INC is seeking an experienced ML engineer to drive edge AI workloads and lightweight model deployment across embedded platforms. You will work with a cross-functional team, optimize inference, and push state-of-the-art techniques for efficiency and latency in real-time AI.

You will mentor junior engineers, publish findings, and contribute to the architecture of next-generation in-memory computing solutions, spanning edge devices to data centers, in a fast-paced startup environment.

Competenze

• 5+ years of experience or PhD in CS, EE, or related fields.
• Strong background in machine learning with edge AI and lightweight deployment.
• Experience with PyTorch, TensorFlow, and JAX.
• Proficiency in C/C++ and Python; ML model optimization.
• Ability to work independently and mentor juniors.

Mansioni

• Develop, optimize, and deploy lightweight ML models for edge AI.
• Implement ML models on embedded platforms (FPGA, ASIC).
• Collaborate with hardware and software teams to productionize models.
• Research state-of-the-art ML techniques to improve efficiency, latency, and power.
• Improve inference efficiency and model compression (quantization, pruning, distillation).
• Partner with cross-functional teams to influence system architecture.
• Provide technical leadership and mentorship to junior engineers.
• Publish findings, present at conferences, and contribute to open-source when applicable.

Conoscenze

Edge AI

Model deployment

Python

C/C++

ML frameworks

Mentorship

Formazione

PhD in Computer Science or Electrical Engineering

Bachelor’s/Master’s in related field

Strumenti

PyTorch

TensorFlow

JAX

ONNX

TensorRT

TFLite/LiteRT

ncnn

CoreML

Descrizione del lavoro

TetraMem INC is seeking an experienced ML engineer to drive edge AI workloads and lightweight model deployment across embedded platforms. You will work with a cross-functional team, optimize inference, and push state-of-the-art techniques for efficiency and latency in real-time AI.

You will mentor junior engineers, publish findings, and contribute to the architecture of next-generation in-memory computing solutions, spanning edge devices to data centers, in a fast-paced startup environment.