Required Skill Sets on top of the above skills:

AppLab Systems Inc
San Jose, United States of America
2 days ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English

Job location

San Jose, United States of America

Tech stack

Microsoft Windows
API
Apple Mac Systems
Unit Testing
C++
Ubuntu (Operating System)
Computer Engineering
System Configuration
Serialization
Data Structures
Data Visualization
Software Debugging
Github
Python
Machine Learning
NumPy
TensorFlow
Software Engineering
Jupyter Notebook
PyTorch
Parallel Computation
Backend
Keras
Pandas
Matplotlib
Core Data
Scikit Learn
Information Technology
Machine Learning Operations

Job description

  • 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

Requirements

  • 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, * 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, * 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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