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AIML - Machine Learning Engineer, Siri & Information Intelligence

AIML - Machine Learning Engineer, Siri & Information IntelligenceSeattle, Washington, United StatesMachine Learning and AIDo you want to make Siri and Apple products smarter for our users? The Siri and Information Intelligence team is redefining how hundreds of millions of people use their devices to get information. We are an Applied ML team pushing the limits on realtime augmented information retrieval and generation, information safety and search technologies, while also responsible for a few user facing production services. We are part of a wider effort to power information across a variety of Apple products – including Siri, Spotlight, Safari, Messages, Lookup, and more.DescriptionAs a member of our fast-paced group, you’ll have the unique and rewarding opportunity to shape upcoming products from Apple. Our team includes a diversity of backgrounds from applied machine learning engineers with a focus on ML and LLM to experienced distributed systems engineers. As such, we are looking for candidates with applied machine learning experience and strong software engineering skills. You will be using and improving upon the latest advancements in machine learning and software engineering to understand user queries and intents, retrieve and rank documents and generate helpful answers and work on horizontal areas on data generation, evaluation and safety.Minimum QualificationsBS in Computer Science, Artificial Intelligence, Machine Learning, Information Retrieval, Data Science or related field or equivalent work experience2+ years of industry related experience, working in collaborative environmentsExperience with using: PyTorch, TensorFlow, or JAX for training and deploying deep learning modelsUnderstanding product requirements then translating them into modeling tasks and engineering tasksProficient in at least two programming languages such as: C/C++, Go, Python, JavaKey QualificationsPreferred QualificationsAdvance degree in Computer Science, Artificial Intelligence, Machine Learning, Information Retrieval, Data Science or related field or equivalent work experienceAnalyzing search ranking and relevance requirements issues and opportunitiesBuilding machine-learned models for search relevance ranking query understanding and questioningEducation & ExperienceAdditional RequirementsPay & BenefitsAt Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $135,400 and $250,600, and your base pay will depend on your skills, qualifications, experience, and location. Apple employees also have the opportunity to become an Apple shareholder through participation in Apple’s discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple’s Employee Stock Purchase Plan. You’ll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses — including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits.Apple is an equal opportunity employer that is committed to inclusion and diversity. We take affirmative action to ensure equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics.#J-18808-Ljbffr
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What You Should Know About AIML - Machine Learning Engineer, Siri & Information Intelligence, Apple

Are you ready to join Apple as an AIML - Machine Learning Engineer for the Siri & Information Intelligence team in beautiful Poulsbo, WA? This is an exciting opportunity for anyone eager to push the boundaries of artificial intelligence while making a real impact on how users interact with their devices. Your work will help improve Siri’s capabilities and those of other Apple products by developing advanced machine learning models and systems. You’ll collaborate with a talented and diverse team of machine learning engineers and software developers, where you’ll contribute to cutting-edge technologies like real-time information retrieval and augmented data generation. Ideal candidates will have a solid foundation in machine learning and software engineering, and a knack for understanding product requirements. If you have experience with PyTorch, TensorFlow, or JAX and can communicate effectively in languages like Python or C/C++, we want to hear from you! With a role that ranges from training deep learning models to evaluating search relevance and safety, your contributions could redefine user experiences for millions. Plus, at Apple, you will be rewarded not just with competitive pay but also with comprehensive benefits that support both professional and personal growth. Come be a part of our mission to enhance intelligence across Apple products and drive innovation forward!

Frequently Asked Questions (FAQs) for AIML - Machine Learning Engineer, Siri & Information Intelligence Role at Apple
What are the responsibilities of an AIML - Machine Learning Engineer at Apple?

As an AIML - Machine Learning Engineer at Apple, you will focus on enhancing Siri and various Apple services through sophisticated machine learning systems. Your principal responsibilities will include developing and deploying deep learning models using frameworks like PyTorch and TensorFlow. You'll analyze user queries to understand intents better, retrieve relevant documents, and generate meaningful responses, thus ensuring users get the most out of their Apple products.

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What qualifications do I need to become an AIML - Machine Learning Engineer at Apple?

To succeed as an AIML - Machine Learning Engineer at Apple, you should ideally hold a BS in Computer Science or a related field and possess at least two years of relevant industry experience. We prioritize candidates who have a strong grasp of deep learning frameworks like PyTorch, TensorFlow, or JAX, and proficiency in programming languages such as Python and C/C++. An advanced degree and experience in analyzing search relevance and ranking can give you an edge.

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How can I prepare for the AIML - Machine Learning Engineer interview at Apple?

Preparation for the AIML - Machine Learning Engineer interview at Apple requires a solid grasp of machine learning concepts and practical experience using related tools and technologies. You should also familiarize yourself with the insights gained from your past projects and be ready to discuss how you translated product requirements into effective machine-learning tasks.

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What is the work environment like for an AIML - Machine Learning Engineer at Apple?

Working as an AIML - Machine Learning Engineer at Apple offers a fast-paced, innovative environment that fosters collaboration and creativity. You'll have the chance to work on impactful projects alongside a diverse team of experts, encouraging an exchange of ideas that drives technological advancement in AI and enriches user experiences worldwide.

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What are the potential career growth opportunities for an AIML - Machine Learning Engineer at Apple?

An AIML - Machine Learning Engineer at Apple can expect numerous opportunities for career growth. As part of a leading tech company, you will have access to workshops, training, and mentoring programs that enhance your skills. You're also open to progressing into more advanced roles within AI engineering and leadership, driving the ongoing technological advancements that shape Apple's products.

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Common Interview Questions for AIML - Machine Learning Engineer, Siri & Information Intelligence
Can you explain a machine learning project you've worked on?

When answering this question, provide a structured overview of a machine learning project, including the problem you aimed to solve, the dataset you used, the algorithms implemented, and your specific contributions. Highlight any challenges you faced during the project and how you overcame them, finishing with the results that were achieved.

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What is your experience with real-time information retrieval?

Highlight your background in designing and implementing systems for real-time data access and processing. Discuss specific challenges you encountered in previous roles and how your approaches optimized user query response times or improved the accuracy of information delivery.

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How do you ensure the safety and ethical considerations of AI models?

Demonstrate your awareness of the ethical implications of AI by discussing methods for bias mitigation, transparency, and fairness in models. Share specific strategies you've implemented to ensure data privacy and enhance the user experience while maintaining safety standards.

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What machine learning frameworks do you prefer and why?

Mention the frameworks you’re most familiar with, such as PyTorch, TensorFlow, or JAX, and explain your reasons for favoring them. Discuss specific functionalities or projects where these frameworks provided uniquely valuable features or performance enhancements.

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How do you approach debugging complex machine learning models?

Your answer should reflect a systematic approach to debugging that includes steps like reviewing data pipelines, retraining models, and visualizing performance metrics. Emphasize your ability to iterate and troubleshoot effectively to enhance model accuracy and functionality.

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What methods do you use for feature selection and engineering?

Discuss your preferred methods for feature selection and engineering, such as statistical tests, recursive feature elimination, or domain knowledge. Illustrate how these methods have improved the performance of your machine learning models in previous projects.

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Can you explain the difference between supervised and unsupervised learning?

Clearly articulate the distinctions between supervised and unsupervised learning, providing examples of each. This understanding reflects your foundational knowledge in machine learning and sets the stage for deeper discussions on applications and algorithms.

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What is your experience with deploying machine learning models into production?

Share your experience deploying models into production environments using tools like Docker, Kubernetes, or cloud-based solutions. Discuss challenges you've faced during deployment and how you addressed them to ensure scalability and reliability of the models.

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How do you stay current with advancements in machine learning?

Express your commitment to continuous learning by mentioning resources you follow, such as prominent journals, conferences, or online courses. Highlight specific areas of interest within machine learning that you're currently exploring and how that knowledge can benefit your role.

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How do you handle collaboration with cross-functional teams?

Focus on your interpersonal skills, emphasizing your ability to communicate complex technical concepts to non-technical stakeholders. Provide examples of successful collaborations in previous roles where you worked with different teams to achieve shared goals, illustrating the importance of collaboration in successful project outcomes.

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CULTURE VALUES
Inclusive & Diverse
Diversity of Opinions
Work/Life Harmony
Dare to be Different
Reward & Recognition
Empathetic
Take Risks
Growth & Learning
Transparent & Candid
Mission Driven
Passion for Exploration
Feedback Forward
BENEFITS & PERKS
Medical Insurance
Dental Insurance
Vision Insurance
Mental Health Resources
Life insurance
Disability Insurance
Health Savings Account (HSA)
Flexible Spending Account (FSA)
Learning & Development
Paid Time-Off
Maternity Leave
Social Gatherings
FUNDING
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EMPLOYMENT TYPE
Full-time, on-site
DATE POSTED
December 23, 2024

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