Role: Data Engineer - Artificial Intelligence & Machine Learning Location Options: Bay Area - CA Responsibilities: - 1. Develop AI/ML Models: * Design, build, and train machine learning models using appropriate algorithms (e.g., supervised, unsupervised, reinforcement learning, deep learning). * Use various machine learning and AI frameworks (TensorFlow, PyTorch, Scikit-learn, Keras, etc.) to implement models. * Experiment with different approaches (e.g., decision trees, neural networks, ensemble methods) and optimize for the best performance. * Perform model selection, training, tuning, and validation using real-world data to achieve the most accurate results. 2. Data Preparation & Feature Engineering: * Clean, preprocess, and structure raw data for analysis, ensuring it is suitable for model training. * Implement data augmentation techniques, handle missing data, and remove outliers. * Engineer features that will improve model performance, understanding the data's underlying relationships. 3. Algorithm Design and Optimization: * Develop and optimize algorithms for specific use cases like image recognition, natural language processing (NLP), speech recognition, or recommendation systems. * Optimize algorithms for high efficiency, scalability, and real-time performance. * Regularly assess and improve model accuracy by experimenting with various hyperparameters, architectures, and optimization techniques. 4. Deploy Machine Learning Models: * Collaborate with software engineers to deploy machine learning models into production environments, integrating them with existing systems. * Ensure the models are scalable, performant, and able to handle real-time or batch data as required. * Implement model monitoring and performance tracking tools to evaluate accuracy and detect any model drift over time. 5. Model Evaluation & Testing: * Use cross-validation and other techniques to evaluate the model's generalization capabilities. * Implement performance metrics to measure model accuracy, precision, recall, F1-score, and other relevant metrics based on project needs. * Perform A/B testing and compare the performance of multiple models. 6. Continuous Improvement & Research: * Stay up-to-date with the latest AI/ML research and advancements in the field, such as new algorithms, architectures, and technologies. * Participate in code reviews and contribute to best practices in AI/ML development. * Experiment with new AI and machine learning techniques to continually improve performance and solve complex problems. 7. Collaboration & Communication: * Work closely with Data Scientists, Software Engineers, Product Managers, and other stakeholders to understand business problems and translate them into machine learning tasks. * Communicate findings, insights, and progress to non-technical stakeholders in a clear, understandable manner. * Collaborate on projects, providing expertise on AI/ML concepts to help shape product features or solutions. 8. Ethical Considerations and Bias Mitigation: * Ensure that the models and algorithms are free from biases and ethically sound, particularly when dealing with sensitive data. * Evaluate fairness, transparency, and interpretability of models, especially in critical applications like healthcare, finance, and legal sectors. 9. Documentation: * Document the model-building process, algorithm choice, and data used, ensuring reproducibility and transparency. * Write clear technical documentation and user guides to facilitate collaboration and knowledge transfer. 10. Innovation and Prototyping: * Prototype AI-driven solutions to demonstrate their potential and feasibility. * Develop proof of concepts (PoCs) and new algorithms for emerging AI and ML technologies (e.g., federated learning, reinforcement learning, generative models) Qualifications: 1.Educational Background: Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, Mathematics, or a related field. Ph.D. in a relevant field is a plus but not required 2.Technical Skills: * Strong programming skills in Python, R, or similar languages for machine learning and data analysis. * Deep knowledge of machine learning libraries such as TensorFlow, PyTorch, Scikit-learn, Keras, and XGBoost. * Strong foundation in linear algebra, probability, statistics, and optimization techniques. * Proficiency in algorithms and data structures. * Experience with deep learning techniques, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, and reinforcement learning. * Familiarity with NLP techniques, computer vision, time series analysis, and other AI sub-domains. * Knowledge of data preprocessing, feature extraction, and feature selection techniques. * Proficiency in cloud platforms (AWS, Azure, Google Cloud) for model training and deployment. * Experience with containerization and orchestration tools (e.g., Docker, Kubernetes) is a plus. * Familiarity with big data technologies (Hadoop, Spark, etc.) is a plus. 3.Soft Skills: * Strong problem-solving and analytical skills. * Ability to work in a collaborative, cross-functional environment. * Effective communication skills to explain complex AI/ML concepts to non-technical stakeholders. * Strong attention to detail and ability to troubleshoot and debug code. * Passion for continuous learning and staying up-to-date with AI and ML advancements. 4.Experience: * Proven experience (3+ years) in developing and deploying machine learning or AI models in a production environment. * Familiarity with MLOps (machine learning operations) principles, such as model versioning, CI/CD pipelines for ML, and model monitoring in production 5.Preferred Qualifications: * Experience with reinforcement learning, unsupervised learning, or generative models (e.g., GANs). * Knowledge of ethics in AI, such as mitigating bias and ensuring fairness in models. Familiarity with NLP libraries like SpaCy, NLTK, Hugging Face Transformers, etc. * Experience in building and deploying AI-powered products in a commercial setting. * Knowledge of edge computing and deploying AI models on edge devices 6.Work Environment: * Collaborative and fast-paced work environment. * Opportunity to work with state-of-the-art technologies. * Supportive and dynamic team culture * The position may require collaborating across multiple teams, including product, engineering, and research groups, to develop and implement AI/ML solutions S alary Range: $149,500 - $224,250 #LI-AD1