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

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