Sequoia Senior Machine Learning Engineer - Automation for Translation and Multilingual Intelligence
Summary
Senior ML engineer on Apple's Translation & Multilingual Intelligence team, building scalable model automation pipelines and ML CI/CD for training, fine-tuning, and deploying translation and multilingual models at scale. Core stack includes Python, distributed computing (Spark, Ray, Dask), and LLM serving/training tools like vLLM and Megatron-LM.
Play a part in the next revolution in human-computer interaction. Build groundbreaking technology for large scale systems, spoken language, big data, and artificial intelligence. The AI/ML - Translation & Multilingual Intelligence team is looking for exceptional Machine Learning Engineers passionate about delighting customer's experience, building and improving the Machine Learning Automation and Tooling with a strong focus on model automation pipelines development and deployment.
You will be part of a team that's responsible for a wide variety of language technologies related development activities. Your focus will be on developing the model automation pipelines which are highly scalable, robust and efficient. 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.
Minimum Qualifications
- 3+ 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 skills
Preferred Qualifications
- Strong 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
