Staff Machine Learning Engineer

Role Overview

• 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 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
• 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

Tech Stack

• BigQuery
• Cloud
• Google Cloud Platform
• Linux
• Pandas
• Python
• PyTorch
• Scikit-Learn
• SQL
• Tensorflow
• Unix

Benefits

• Competitive salary
• Health insurance
• Paid time off
• Professional development opportunities