Deep Learning Engineer

Deep Learning Engineer

Responsibilities

The Deep Learning Engineer will be responsible for developing and optimizing deep learning algorithms, researching new AI technologies, implementing neural network models, and collaborating with cross-functional teams to integrate AI solutions into various products.

Day-to-day tasks include exploring and implementing cutting-edge algorithms, leading the entire model lifecycle from training and optimization to quantization and deployment, collaborating with the team to solve core technical challenges in our products, and staying current with the latest trends and developments in the deep learning field.

Qualifications

- Proficiency in Python and a basic foundation in C++.

- Familiarity with at least one deep learning framework, such as PyTorch or TensorFlow.

- Proficiency in DNN development and various algorithms, e.g., understanding of (self)-supervised training, GAN-based adversarial loss, backbone design, DDP distributed training, etc.

- Familiarity and practical experience with model quantization (QAT and PTQ).

- Excellent mathematical skills, such as a strong foundation in linear algebra, probability, calculus, analysis, and optimization methods.

- Experience in academic research and the ability to keep track of the latest advancements in the AI field.

(Plus) Understanding of the Transformer architecture and its acceleration/quantization methods.

(Plus) Understanding of denoised diffusion or flow matching models.

(Plus) Familiarity with the voice domain (e.g., speech recognition, speaker verification, noise reduction, beamforming, etc.), which will allow you to integrate into our core business and make a key impact more quickly.