Job Open | AI/ML Engineer | San Leandro, CA Hybrid

ApTask - San Leandro, CA

Full-time - Mid Level

San Leandro, CA

Administrative and Support Services

About the position

The AI/ML Engineer will be responsible for designing, developing, and implementing advanced machine learning algorithms and AI solutions to enhance decision-making across various applications. This role involves collaboration with cross-functional teams to create scalable models that address complex real-world challenges.

Responsibilities

• Design, implement, and optimize state-of-the-art AI/ML algorithms for predictive modeling, computer vision, natural language processing (NLP), and other domains.
• Develop, train, and evaluate machine learning models, ensuring high accuracy, robustness, and performance in production environments.
• Collaborate with data engineers to preprocess, clean, and augment large datasets to improve the quality and reliability of AI/ML models.
• Apply domain knowledge to select and engineer the most effective features that improve model performance.
• Fine-tune models using hyperparameter optimization techniques such as grid search, random search, or Bayesian optimization.
• Stay up-to-date with the latest research and advancements in AI/ML, and experiment with novel algorithms to push the boundaries of current solutions.
• Collaborate with software engineering teams to deploy models in production and integrate AI/ML solutions into existing systems and products.
• Monitor deployed models for drift, accuracy, and other performance metrics, and update models as necessary.
• Work in a highly collaborative environment with data scientists, product managers, and stakeholders to translate business requirements into AI-driven solutions.

Requirements

• Bachelor's or Master's degree in Computer Science, Electrical Engineering, Data Science, Mathematics, or a related field (PhD is a plus).
• 3+ years of experience in AI/ML algorithm development and implementation.
• Proficiency in Python, R, or C++ for AI/ML development. Experience with libraries such as TensorFlow, PyTorch, Scikit-learn, Keras, or XGBoost.
• Strong understanding of supervised, unsupervised, and reinforcement learning techniques (e.g., classification, regression, clustering, neural networks, deep learning, decision trees, SVMs, and ensemble methods).
• Solid understanding of linear algebra, probability, statistics, and optimization techniques commonly used in machine learning algorithms.
• Experience working with large-scale datasets and knowledge of data processing tools such as Spark, Hadoop, or Apache Flink.
• Experience with model validation techniques such as cross-validation, confusion matrices, ROC/AUC curves, and metrics like precision, recall, F1-score, and accuracy.
• Experience deploying AI/ML models on cloud platforms such as AWS, Google Cloud, or Microsoft Azure.
• Familiarity with version control tools (Git) and CI/CD pipelines for model deployment.
• Strong analytical and problem-solving skills to tackle complex challenges using AI/ML methods.
• Ability to work effectively in a team environment and communicate technical concepts to non-technical stakeholders.