ML Engineer - Inference

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Requirements:

• Strong background in machine learning, with hands-on experience in developing and deploying inference models.
• Proficiency in Python and machine learning frameworks such as TensorFlow or PyTorch.
• Experience with optimization techniques including quantization, pruning, and model compression.
• Strong problem-solving skills for troubleshooting complex technical issues.
• Excellent communication and collaboration skills in a remote team environment.
• Passion for learning and staying updated on advancements in ML inference technologies.
• Experience with ML workflow libraries like CUDNN/TensorRT, ROCm, OpenVino, or OpenPPL; knowledge of ML communication frameworks like NCCL is an advantage.

Responsibilities:

• Design and implement efficient algorithms and models for real-time inference in Conversation AI applications, with a focus on Nebula.
• Collaborate with cross-functional teams to integrate machine learning models into production systems, ensuring scalability, reliability, and performance.
• Optimize and fine-tune machine learning models for resource-constrained environments.
• Develop monitoring and evaluation mechanisms to assess performance of inference models.
• Stay updated on advancements in machine learning inference techniques and incorporate new approaches as needed.
• Contribute to the documentation of best practices for implementing and deploying ML inference solutions.