ML - MACHINE LEARNING ENGINEER

We aim to improve the World by using data and AI.

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Our Machine Learning Engineers are passionate about efficiently running cutting-edge AI models on a variety of hardware platforms and architectures. You will work closely with Researchers and Software Engineers, making AI prototypes become production-ready software and applying state-of-the-art techniques to optimize models for deployment while maintaining accuracy.

at Sertis Bangkok

• Researching latest trends and technologies to improve our algorithms and design a future-proof solution

• Taking computer vision algorithms and prototypes and making them become fully-fledged applications

• Improving existing machine learning software and optimizing models for deployment on multiple hardware and architectures

• Deploying models to IoT devices and cloud platforms while maintaining our existing frameworks and infrastructure

• Developing model optimization frameworks and automating model conversions and releases

• Learning and sharing expertise with the team (documentation, code reviews, demos, etc.)

Our Machine Learning Engineers (you) will be:

What you will need:

• 2+ years of software development experience

• Knowledge of multiple programming languages, ideally including Python and C++

• Familiarity with Linux environments

• Demonstrated understanding of deep learning and data science concepts

• Experience with multiple deep learning frameworks (e.g. TF, Pytorch, MXNet, CAFFE, ONNX)

• Willingness to take end-to-end ownership of problems that span across multiple disciplines and stacks

• Be passionate about best-practices in software development

It's a plus if you have experience with:

• Experience working with IoT and edge devices (e.g. Android, Nvidia Jetson)

• Experience with deep learning compilers (e.g TVM, XLA, Glow)

• Experience with deep learning accelerators (e.g. TPU, NPU, DLA)

• Experience with deep learning optimization frameworks (e.g. TRT, TFLITE, MACE)

• Experience with Docker, CI/CD, Cloud Platforms (e.g. GCP, AWS, Azure)

• Understanding of low-level code performance optimization