Machine Learning Engineer, Natural Language Generation
Apply NowCompany: Apple
Location: Seattle, WA 98115
Description:
Summary
On the Input Experience NLP team, we build the language models that underpin intelligent text input and composition across Apple platforms, from keyboard autocorrection to the Writing Tools and Smart Reply features announced at WWDC 2024. We believe that generative AI is an incredibly promising technology that can help people communicate effectively and express themselves clearly, and we have only just begun to incorporate this technology into our products. On our team, you will help build the future and have a voice in what shape it takes.
We are looking for a Machine Learning Engineer to help our ML models better serve the full range of our customers, across languages, writing styles, and other personal context, in a privacy-preserving way. You will build and refine the training and evaluation pipelines that define our slice of Apple Intelligence, driving the focused iteration that makes the user experience magical. You will join an ambitious, organized, and collaborative team in a unique position to integrate the latest innovations from the ML community and work on features that reach everyday users, including your family and friends. You'll work closely with teams across Apple, collaborating on human interfaces, user studies, internationalization, ML technologies, system integration, and more.
Apple is where individual imaginations gather together, committing to the values that lead to great work. Every new product we build, service we create, or Apple Store experience we deliver is the result of us making each other's ideas stronger. That happens because every one of us shares a belief that we can make something wonderful and share it with the world, changing lives for the better. It's the diversity of our people and their thinking that inspires the innovation that runs through everything we do. When we bring everybody in, we can do the best work of our lives. Here, you'll do more than join something-you'll add something.
Description
As a Machine Learning Engineer on our team, you will enable next-generation AI applications by building toolkits and workflows that scale to increasingly sophisticated modeling tasks. You will design the abstractions and implement the algorithms that facilitate efficient data synthesis, data curation, prompt engineering, and model evaluation. You will contribute to a company-wide effort to build robust modeling pipelines that optimize our ability to iterate rapidly and continuously deliver improvements to our customers. Finally, you will help define and refine new features that expand both the depth of Apple Intelligence's capabilities and the breadth of its support for the full spectrum of Apple customers.
Key responsibilities:
* Development and maintenance of data and model pipelines that scale to deployment in production
* Building toolkits for iterating on model quality via data synthesis and prompt engineering
* Definition of robust automated evaluation mechanisms to facilitate hillclimbing on model quality
* Failure analysis from user feedback to understand shortcomings of our models and evaluation data
* Research into state-of-the-art techniques for improving model quality and robustness
* Implementation of experiments and simulations to assess the value of model changes
On the Input Experience NLP team, we build the language models that underpin intelligent text input and composition across Apple platforms, from keyboard autocorrection to the Writing Tools and Smart Reply features announced at WWDC 2024. We believe that generative AI is an incredibly promising technology that can help people communicate effectively and express themselves clearly, and we have only just begun to incorporate this technology into our products. On our team, you will help build the future and have a voice in what shape it takes.
We are looking for a Machine Learning Engineer to help our ML models better serve the full range of our customers, across languages, writing styles, and other personal context, in a privacy-preserving way. You will build and refine the training and evaluation pipelines that define our slice of Apple Intelligence, driving the focused iteration that makes the user experience magical. You will join an ambitious, organized, and collaborative team in a unique position to integrate the latest innovations from the ML community and work on features that reach everyday users, including your family and friends. You'll work closely with teams across Apple, collaborating on human interfaces, user studies, internationalization, ML technologies, system integration, and more.
Apple is where individual imaginations gather together, committing to the values that lead to great work. Every new product we build, service we create, or Apple Store experience we deliver is the result of us making each other's ideas stronger. That happens because every one of us shares a belief that we can make something wonderful and share it with the world, changing lives for the better. It's the diversity of our people and their thinking that inspires the innovation that runs through everything we do. When we bring everybody in, we can do the best work of our lives. Here, you'll do more than join something-you'll add something.
Description
As a Machine Learning Engineer on our team, you will enable next-generation AI applications by building toolkits and workflows that scale to increasingly sophisticated modeling tasks. You will design the abstractions and implement the algorithms that facilitate efficient data synthesis, data curation, prompt engineering, and model evaluation. You will contribute to a company-wide effort to build robust modeling pipelines that optimize our ability to iterate rapidly and continuously deliver improvements to our customers. Finally, you will help define and refine new features that expand both the depth of Apple Intelligence's capabilities and the breadth of its support for the full spectrum of Apple customers.
Key responsibilities:
* Development and maintenance of data and model pipelines that scale to deployment in production
* Building toolkits for iterating on model quality via data synthesis and prompt engineering
* Definition of robust automated evaluation mechanisms to facilitate hillclimbing on model quality
* Failure analysis from user feedback to understand shortcomings of our models and evaluation data
* Research into state-of-the-art techniques for improving model quality and robustness
* Implementation of experiments and simulations to assess the value of model changes