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AIML - Senior Machine Learning Engineer, Siri and Information Intelligence

United Statesfull_timeVerifiedPosted 12 Oct 2024

💰 $250,600/yr($135,400/yr – $250,600/yr)

About the role

Summary

Posted: Oct 12, 2024
Weekly Hours: 40
Role Number:200573121

As part of Apple Intelligence, Siri team is at the forefront of the next revolution in machine learning and NLP. Our innovative product redefines computing by leveraging cutting-edge technologies in Natural Language Understanding and Conversational AI. We are dedicated to creating groundbreaking conversational assistant technologies for both large-scale systems and new client devices, building upon our legacy of intelligent assistant solutions that already assist millions of users worldwide. We are seeking a highly skilled Senior Machine Learning Engineer specializing in NLP/Conversational AI to join our dynamic team. The ideal candidate will play a pivotal role in the evaluation and enhancement of our Apple Intelligence products. They will collaborate closely with cross-functional teams to implement and optimize models, ensuring the highest level of performance, accuracy, and scalability. This role offers an exciting opportunity to contribute to the advancement of AI systems and shape the future of computing. As a Machine Learning Engineer within the Siri team, you will define new approaches for evaluating state-of-the-art ML based systems, conversational AI, and model interpretability. You will collaborate with different modeling teams, and product managers to conduct thorough evaluation and analysis of Apple Intelligence products and models, identifying areas for improvement and optimization. You will work with large amounts of real-world data to analyze and propose changes to the Siri user experience. You will ensure data quality throughout all stages of acquisition and processing, data wrangling, etc. Your expertise in defining and measuring the online and offline end-to-end metrics will help communicate, evaluate, and iterate on state-of-the-art deployed models and predictors

Description

As Siri is becoming increasingly complex AI system, it is critical to understand the impact of each ML model on other dependent models, while assessing the impact on Siri end-user experience. The Siri team leads the development of advanced evaluation methodologies for ML based systems, model interpretability, and experimentation, to ensure that every release delivers an improved Siri user experience. The team is searching for talented ML Engineers to work with a passionate, product-focused team to define new approaches for evaluating ML based systems, conversational AI, and model interpretability You will run experiments, statistically interpret data with a mind on causation, data visualization, plus designing, building, and evaluating models. You will work with large amounts of real-world data to analyze and propose changes to Siri user experience. You will ensure data quality throughout all stages of acquisition and processing, data wrangling, etc. Your expertise in defining and measuring the online and offline end-to-end metrics will help communicate, evaluate, and iterate on state-of-the-art deployed models and predictors.

Minimum Qualifications

7+ years of professional work experience applying machine learning to real-world problems, and crafting scalable and effective data solutions.
Strong domain knowledge in at least one of the following: NLP, conversational AI, speech recognition
Excellent programming skills in Python and data analytical skills.
Excellent problem solving, critical thinking, and communication skills.

Preferred Qualifications

Strong attention to detail. Proven ability to dive into data to discover hidden patterns and conduct error/deviation analysis
Good experience with applying Big Data tools (MapReduce, Hadoop, Hive and/or Pig, Spark) to large quantities of textual data
Domain knowledge in applied statistics and experimental design is big plus!
Enthusiasm for continuing to learn state-of-the-art techniques in machine learning/data science
Strong background in A/B testing procedure, causal analysis, and cohort analysis.
Exposure to model interpretability techniques and their real-world advantages/drawbacks
Good Conversational AI domain knowledge