Developing automation pipelines and tools for training, evaluating and deploying machine learning models for machine translation and related NLP tasks Implementing and optimizing ML pipelines with emphasis on distributed data processing, training, inference throughput and efficiency Collaborating with software engineers and QE to integrate ML models into production systems3+ years working experience in ML lifecycle, Model management, data processing, large distributed system & cloud computing such as Spark, Ray and Dask Proficient coding skills in Python and experience with ML infrastructure tools such as vLLM, SGLang, TRT-llm, Slime or Megatron-LM Excellent communication and problem solving skillsStrong machine learning expertise with hands-on experience in model fine-tuning, evaluation, experiment tracking, pipeline building and deployment is a big plus Experience with LLMs, neural machine translation is a plus Deep knowledge in ML frameworks and technologies such as NLP, MT, ASR, PyTorch, TensorFlow, JAX, and transformer architectures is a plus. The role will be part of the model automation team to deal with large quantities of data, apply the state-of-the-art methods in deep learning to tackle real world problems, create the production quality models at scale and set up ML CI/CD pipelines.