AIML - Sr Machine Learning Engineer, Data and ML Innovation at Apple

Cupertino, California, United States -

Full Time

Start Date

Immediate

Expiry Date

01 Jan, 26

Salary

0.0

Posted On

03 Oct, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Model Evaluation, Data Processing, Data Augmentation, Software Engineering, Deep Learning, Algorithm Development, Error Analysis, Hyper-Parameter Tuning, Multi-Modal Data, Large Models, Python, PyTorch, TensorFlow, JAX, Creative Thinking, Critical Thinking

Industry

Computers and Electronics Manufacturing

Description

Do you want to play a part in the next revolution in Foundation Models? Contribute to model hill climbing for Apple Intelligence features that leverage Apple Foundation Models, and work with the people who built the intelligent products that helps millions of people get things done — just by asking or typing! The vision for the AI/ML FM Data organization is to improve Foundation Models by leveraging data from a variety of sources: crawl, license, vendor and internal crowd-sourcing. As a Sr ML Engineering on the team, you will drive ML innovations, identify key opportunity areas where data can play a crucial role and experiment with various data augmentation strategies to improve model training efficiency and performance.. DESCRIPTION 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. MINIMUM QUALIFICATIONS 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. PREFERRED QUALIFICATIONS 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

Drive ML innovations and identify key opportunity areas where data can play a crucial role. Experiment with various data augmentation strategies to improve model training efficiency and performance.