Backend Software Engineer, Inference Optimization Specialist

2 months ago


Singapur, Singapore Hireio, Inc. Full time
About the Team

Hireio, Inc.'s Trust and Safety R&D team is a rapidly growing unit responsible for developing machine learning models and systems to identify and prevent internet abuse and fraud on our platform. Our team's continuous efforts enable our global popular app to provide the best user experience and bring joy to users worldwide.

Our mission is to bridge the gap between algorithm models and business scenarios, efficiently and stably applying TnS's algorithmic capabilities to business needs. We aim to cover all areas where our app requires algorithmic capabilities.

Responsibilities:
  1. Collaborate closely with business teams to optimize algorithm application integration plans, improve efficiency in evaluating and using algorithm applications across various business scenarios, and reduce the cost of managing and optimizing algorithm applications in different business scenarios.
  2. Be responsible for the architectural design, development, and performance tuning of algorithm applications, solving technical challenges such as high concurrency, high reliability, and high scalability. This includes multiple sub-areas: resource scheduling, task orchestration, model training, model inference, model management, dataset management, workflow orchestration, etc.
  3. Research and introduce cutting-edge engineering technologies related to algorithms.
Requirements

Qualifications

  • Bachelor's degree in Computer Science, Engineering, or equivalent practical experience.
  • Master at least one of the following languages in the Linux environment: C/C++, Python, Go.
  • Master at least one state-of-the-art machine learning framework (e.g., Tensorflow, Pytorch).
  • Strong analytical abilities and problem-solving skills.
Preferred Qualifications

Preferred Qualifications

  • Experience working in large-scale tech companies.
  • Familiarity with model optimization algorithms like quantization and pruning.
  • Practical experience in performance optimization/tuning of deep learning model training/inference.
  • Practical experience in CUDA programming and TensorRT.
  • Familiarity with LLM model inference frameworks like VLLM and TensorRT-LLM.


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