AI/ML Engineer

Required Skill Sets:
- 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.

Location: South 34th Street #87, San Jose, CA, USA

Skills required for this job:

• C++
• Computer engineering
• Computer science
• Convolutional neural network (CNN)
• Data science
• Debugging
• Deep learning
• Jupyter Notebook
• LSTM
• ML
• Machine learning with Python
• Matplotlib
• NumPy
• PyTorch
• Python
• Random neural network (RNN)
• Software design
• TensorFlow
• Ubuntu
• Windows
• macOS
• scikit-learn