Sr. Machine Learning Engineer Pleasanton - CA - California

Hands-on experience on:

1. Programming Languages

Strong Python familiarity (hands-on) for data prep, modeling, and building ML components.

SQL - Skills: joins, window functions, CTEs, query optimization

2. Machine Learning

LinearLogistic Regression

Decision Trees, Random Forest, XGBoost, LightGBM

SVM, KNN

Model evaluation - PrecisionRecall, F1, ROC-AUC, MSE, RMSE

Model tuning - Grid search, randomized search, cross-validation

3. Deep Learning

Frameworks: TensorFlow, Keras, PyTorch

CNNs, RNNs, LSTMs, Transformers

Use cases: NLP, computer vision, time-series forecasting

4. Data Wrangling & Preprocessing

Missing data handling

Feature engineering

Data cleaning

Roles & Responsibilities

Define the ML use case, success metrics, and evaluation criteria Liaise with business directly and translate business needs into an ML approach.

Perform data exploration, data quality checks, feature engineering, and dataset preparation for training and testing.

Build, train, validate, and iterate ML models compare experiments and select the best candidate model.

Package the solution for production (e.g., containerized scoringservice endpoint) and support deployment with engineeringMLOps practices

Set up basic monitoring (model accuracyhealth) and support continuous improvement post-release. Required Skills & Experience

Solid foundation in ML concepts (supervisedunsupervised, evaluation, validation) and practical experimentation.

Experience taking models to production in a cloud-agnostic way (portable design APIservice mindset).

Working knowledge of version control and basic CICD-style collaboration with engineering teams.