AI/ML Engineer

Role:

AI/ML Engineer with Deep Learning Experience, someone who has worked on

Keras Location:

San Jose, CA Required Skill Sets on top of the above skills: Experience in Data Science and DeepLearning frameworks. Customer requirement analysis, cross team collaboration Software Development Lifecycle, strong Software Design/Development experience Computer Science or Computer Engineering or equivalent technical degree must be able to recognize potential issues, and compose technical communications in GitHub) Experience working with Windows, MacOS, and Ubuntu environments Excellent written and oral communication skills Being a team player with a positive attitude and people skills Open to learning new internal technical tools Required Python Skills Python installation, environment setup and Jupyter Notebook Object and Data Structures basics Comparison Operators and Statements Methods and Functions Errors and Exception handling Built-in functions and Python Generators Using scientific Python libraries numpy, pandas, matplotlib, scikit-learn Use data visualization with Python Machine Learning Prerequisites Overview of ML explaining life cycle like Data Acquisition

• Cleaning
• Training a model
• Testing a model
• Evaluating a model Knowledge on deploying models on mobile devices iOS/Android Knowledge on C++ for custom functions and writing unit test cases.

Strong debugging skills on C++/Python code. Basic jargons of ML which include Cost functions, Gradient Descent, Back Propagation, Activation functions etc Supervised, Unsupervised, Reinforcement learning Classifications and Regression Using Datasets Types of algorithms like Decision Tree, K means etc Using scientific Python libraries numpy, pandas, matplotlib, scikit-learn Importing data in python, clean, preprocess data and manipulate data frames with pandas Neural networks, CNN, RNN/LSTM Keras 3

Prerequisites Multi-Backend Installation:

Installing Keras 3 and configuring backends (JAX, PyTorch, or TensorFlow) using the KERAS_BACKEND environment variable.

Core Data Structures:

Understanding Layers, Models, and the fundamental difference between the Sequential API , Functional API , and Model Subclassing .

Backend-Agnostic Ops:

Familiarity with the keras.ops namespace (the cross-framework NumPy-like API) and keras.random for writing framework-independent code.

State Management:

Concepts of statelessness vs. statefulness, especially when working with the JAX backend and Keras 3 s functional layer calls.

Training & Evaluation:

Mastering the high-level .fit(), .evaluate(), and .predict() workflows, as well as writing Custom Training Loops using GradientTape (TF/PyTorch) or jax.grad.

The Distribution API:

Knowledge of keras.distribution for multi-GPU and TPU training (Data Parallelism and Model Parallelism).

Optimization & Compilation:

Understanding XLA (Accelerated Linear Algebra) and how to leverage jit_compile for performance across different hardware.

Serialization:

Using the modern .keras v3 format for saving/loading models across different frameworks and platforms.