AI/ML Engineer

I’m helping Applab Systems Inc find a top candidate to join their team full-time for the role of AI/ML Engineer.

You'll advance deep learning innovation by developing cross-platform, high-performance ML solutions.

Compensation:

Hidden

Location:

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

Mission of Applab Systems Inc:

"To deliver innovative, cost-effective technology and staffing solutions that help businesses achieve their goals through reliable expertise and customer-focused services."

What makes you a strong candidate:

• You are an expert in Machine learning with Python, ML, Deep learning, Data science.
• You are proficient in scikit-learn, macOS, Windows, Ubuntu, TensorFlow, Software design, Random neural network (RNN), Python, PyTorch.
• English - Fully fluent

Responsibilities and more:

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.

Your potential leader(s):

• Srikanth Inampudi - Delivery& Operations Manager at AppLab Systems, Inc