Staff Machine Learning Engineer

Darum lohnt es sich

Responsibilities Collaborates and pairs with other product team members to create secure, reliable, scalable machine learning solutions Works with Product Team to ensure user stories that are developer-ready, easy to understand, and testable Configures commercial off the shelf solutions to align with evolving business needs Creates meaningful dashboards, logging, alerting, and responses to ensure that issues are captured and addressed proactively Participates in learning activities around modern software design, machine learning, and development core practices Researches and analyzes business trends and behavioral data to identify opportunities for improvement and new initiatives Leads the evaluation development and recommendation of specific technology products and platforms Monitors tools and participates in conversations to encourage collaboration across product teams Provides application support for software running in production Proactively reviews the Performance and Capacity of all aspects of production: code, infrastructure, data, message processing, and prediction quality Requirements 3 - 6 years of relevant work experience Strong experience designing, training, evaluating, and deploying machine learning models in production environments Experience with ML lifecycle management, including feature engineering, model versioning, experimentation, validation, and monitoring for data drift and model performance degradation Experience building and operating ML pipelines using cloud-native services, data platforms, and CI/CD practices for reproducible and reliable model deployment Strong understanding of applied statistics, model evaluation metrics, and tradeoffs between model accuracy, interpretability, latency, and operational cost Experience with algorithms such as clustering, forecasting, anomaly detection, and neural networks Experience in advanced machine learning techniques such as NLP, convolutional neural networks, autoencoders, and embedding generation and utilization Experience in training machine learning models with extremely large datasets Experience with Data Analysis and Machine Learning Tools and Libraries like Jupyter Notebooks, Pandas, SciPy, Scikit-learn, Gensim, TensorFlow, PyTorch, etc.

Experience in a modern scripting language (preferably Python) Experience in writing SQL queries against a relational database Experience in version control systems (preferably Git) Experience in a Linux or Unix-based environment Experience in a CI/CD toolchain Experience in production systems design, including High Availability, Disaster Recovery, Performance, Efficiency, and Security Experience in cloud computing platforms and associated automation patterns #J-18808-Ljbffr Experience in Google Cloud Platform and AI/ML-related components such as Vertex AI, BigQueryML, and AutoML Experience in effective data engineering practices and big data platforms such as BigQuery, Data Store, etc.