Synaptics | Hiring | US01 Machine Learning Engineering T3 | Irvine, CA | United States | BigDataKB.com | 7 Oct 2022

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Job Location: Irvine, CA

Overview:
Millions of people experience Synaptics every day. Our technology impacts how people see, hear, touch, and engage with a wide range of IoT applications – at home, at work, in the car or on the go.

We solve complex challenges alongside the most influential companies in the industry, using the most advanced algorithms in areas such as machine learning, biometrics and video processing, combined with world class software and silicon development.

Responsibilities:
Track the progress of tasks and assign tasks among group. Responsible for bug tracking,

maintenance, and adding new features to the existing framework. Engage responsibly in employing an

existing machine learning training and tuning framework to generate audio models required for

integration into custom products. Interaction with other members of the research and development team,

marketing, and applications engineering. Tracking tuning activities, documentation, and devising an

organization scheme to manage multiple tuning projects. Using the latest advances in machine learning,

data science, neural network, and deep learning to enhance our products and create delightful user

experiences. Developing techniques to minimize the footprint of trained ML models with various pruning,

compression, and real-time adaptation techniques. Developing strategies to balance long and short-term

business objectives. Supervising small teams including the test team and ML engineers to develop and

evaluate the performance of solution in real environments and improve robustness.

Qualifications:
Masterโ€™s or foreign equivalent degree in Electrical/Electronics Engineering,

Computer Science, Computational or Data Sciences or a related field and two (2) years of experience in

the job offered or related occupation. Experience must include one (1) year of experience with: 1. Python.

2. C++. 3. Linux. 4. Familiarity with machine learning frameworks (Keras, PyTorch and Tensorflow) and

python libraries (like scikit-learn). 5. Deep knowledge of math, probability, statistics, and algorithms. 6.

Understanding of data structures, data modeling and software architecture. 7. Experience with

product/services parallel computing, accelerator architecture, CUDA, CUDNN, TensorRT libraries. 8.

Experience in deploying perception algorithms into real world environments and outstanding analytical

and problem-solving skills.

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