AIML - Sr Machine Learning Engineer

Apple • Cupertino, CA

7h

About The Position

We are looking for people with a track record in building models and model-driven products to affect user experiences. Join us, and impact hundreds of millions of customers across billions of their interactions with foundation model powered Apple Intelligence features, that are available on iPhone, iPad, HomePod, Mac, Watch, CarPlay, and tv across more than 30 languages. - Algorithm development: Define signals that are important in prompts, responses and CoT reasoning steps. These usually require a fine-tuned model for specific use cases. - Model evaluation: Understand the importance of a balanced eval-set. Ability to perform error analysis to figure out how to improve model capabilities - Ablation experiments: Test your data augmentation strategies via ablation experiments. Comfortable debugging training errors, and tune hyper-parameters and data mixture to achieve desired outcome. - Data processing and data filtering: Ability to efficiently process and filter very large amounts of data, often times messy

Requirements

• 5+ years of hands on ML engineering experiences.
• Master or PhDs in Computer Science, Electric Engineering or Mathematics.
• Have prior experience as an ML modeler/scientist/researcher.
• Knowledgeable in classic machine learning algorithms (SVM, Random Forest, Naive Bayes, KNN etc), as well as comfortable with more modern deep learning frameworks (PyTorch, Tensorflow, Jax).
• Familiarity with multi-modal data and large models including image and video.
• Possess strong software engineering skills and mindset.
• Have a high bar for engineering code quality and scalability.

Nice To Haves

• Hands on experiences with different phases in LLM model training, including LoRA, SFT, RLHF, reward modeling.
• A good communicator with clear and concise, active listening and empathy skills.
• Are self-motivated and curious.
• Strive to continually learn on the job.
• Have demonstrated creative and critical thinking with an innate drive to improve how things work.
• Have a high tolerance for ambiguity.

Responsibilities

• Algorithm development: Define signals that are important in prompts, responses and CoT reasoning steps. These usually require a fine-tuned model for specific use cases.
• Model evaluation: Understand the importance of a balanced eval-set. Ability to perform error analysis to figure out how to improve model capabilities
• Ablation experiments: Test your data augmentation strategies via ablation experiments. Comfortable debugging training errors, and tune hyper-parameters and data mixture to achieve desired outcome.
• Data processing and data filtering: Ability to efficiently process and filter very large amounts of data, often times messy

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level