AI/ML Engineer at Applab Systems Inc | Torre
AI/ML Engineer
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Applab Systems Inc

AI/ML Engineer

Applab Systems Inc
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Emma
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You'll advance deep learning innovation by developing cross-platform, high-performance ML solutions.
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Full-time

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+ Overtime ( USD120 /hour)

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South 34th Street #87, San Jose, CA, USA
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Posted 4 days ago

Requirements and responsibilities

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.
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