As a Machine Learning Research Engineer you will help with initiatives to significantly advance Siris natural language understanding and planning capabilities using innovative LLM CORE RESPONSIBILITIES WILL INCLUDE: Developing innovative systems for synthetic training data generation and implementing strategies for the continuous optimization of model performance. Designing and implementing agentic workflows and RAG systems to enhance Siris capabilities. Optimizing model performance for tool calling and reasoning tasks. Actively staying at the forefront of academic and industry research in LLMs NLP and agentic systems and translating novel insights into practical solutions. Collaborating closely with a multidisciplinary team of researchers software engineers and product designers to seamlessly integrate AI innovations into the Siri user experience.
Advanced degree (MSc/PhD) in Machine Learning Computer Science or a related quantitative field; or BSc with 2 years of relevant industry experience
Hands on experience in machine learning engineering outside of research
Strong Python proficiency including development debugging and design coupled with extensive experience using ML frameworks (e.g. PyTorch Jax HuggingFace)
Excellent problem-solving critical thinking and interpersonal skills with a collaborative attitude
Proven hands-on experience in machine learning engineering for large-scale models with a strong focus on generative AI LLMs Retrieval Augmented Generation (RAG) or agentic systems
Applying LLMs for synthetic data generation (e.g. for knowledge distillation) or applying reinforcement learning for post-training or fine-tuning of LLMs.
A successful track record of building and deploying end-to-end ML data pipelines (data preparation storage training and inference) in cloud or on-premise environments.
Experience with training fine-tuning and deploying LLMs in production environments.
Proficiency in evaluating LLMs for specific product tasks and performance metrics.
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