Development and Maintenance: Create, test, deploy, and maintain scalable, low-latency machine learning products and pipelines. Consider factors such as the nature of the data, problem complexity, and available computational resources.

Model Validation: Ensure model performance on unseen data, generalizing well without overfitting. Conduct thorough testing to identify and address potential issues like bias or fairness concerns.

Platform Development: Design and develop the next-generation machine learning platform to support thousands of concurrent model training pipelines and billions of daily batch predictions.

Research and Innovation: Stay updated with the latest machine learning technologies, pushing the boundaries of what is currently possible. Keep abreast of industry trends and developments.

Experimentation: Experiment with new machine learning platforms tailored to our environment, creating quick prototypes or proof-of-concepts.

Automation: Support the automation of the machine learning pipeline using CI/CD principles, promoting consistency, reproducibility, and agility.

Leadership Opportunity: Depending on your skills and experience, you may have the chance to lead people technically.

• CI/CD and Big Data Tools: Familiarity with CI/CD (e.g., GitHub Actions, Airflow) and ETL or big data tools (e.g., MapReduce, Spark, Flink, Kafka, Docker, Kubernetes).
• Machine Learning Libraries and Cloud Solutions: Familiarity with mainstream ML libraries (e.g., TensorFlow, PyTorch, Spark ML) and/or cloud solutions (e.g., AWS, Sagemaker).
• Programming Skills: Proficiency in Python, Go, or other OOP languages.
• SQL and Databases: Experience with SQL and databases, including SQL query optimization.
• Testing: Experience with unit test frameworks.
• Fundamentals: Familiarity with data structures, algorithms, and software engineering principles.

• Infrastructure as Code and Orchestration Tools: Experience with Infrastructure as Code (Terraform), orchestration tools (Airflow, AWS Step/Lambda), CI/CD pipelines using GitHub Actions, and real-time monitoring/alerting frameworks such as Prometheus and Grafana.
• Machine Learning Frameworks: Hands-on experience with frameworks like TensorFlow, PyTorch, model registry, OpenMetadata, ML feature store, etc.
• Additional Tools: Extensive experience with Snowflake, Snowpark, and Redis/Aerospike.
• Domain Knowledge: Experience with the advertising industry, recommendation systems, or real-time bidding (RTB).