Iterable is looking for a Senior Machine Learning Engineer to architect and develop robust systems for feature engineering and large-scale model training to enhance the AI capabilities of their customer engagement platform.
Requirements
- Proficiency with Python (with a preference for experience in distributed data processing environments like Databricks, Spark, or similar platforms).
- Hands-on experience with large-scale data pipelines, distributed systems, and cloud data storage (Databricks Delta, Spark, Kafka, Postgres, etc.).
- Experience building or operating ML platforms on Databricks.
- Scala development experience
- Familiarity with ML workflow orchestration tools (e.g., MLflow, Kubeflow, Airflow) and interest in automating model development, testing, and deployment.
- Exposure to generative AI or large language model workflows within an agentic or conversational UX context.
- Experience designing developer-facing APIs or tools to empower other ML engineers or data scientists.
Responsibilities
- Independently lead large-scale machine learning initiatives—delivering capabilities for scalable feature engineering, data processing, and model training on Databricks.
- Design, build, and deploy machine learning models that enable our partners to reach the right user with the right message at the right time.
- Own the complete lifecycle of ML platform features: from requirements gathering and architecture, through implementation, deployment, and post-launch support.
- Shape architectural decisions aimed at building robust, reusable, and highly available ML infrastructure that raises the bar for engineering and data science excellence.
- Mentor colleagues through code reviews, technical design sessions, and knowledge sharing, helping grow a strong culture of engineering rigor and learning.
Other
- Have 5+ years of experience in machine learning engineering, data infrastructure, or platform engineering, preferably in a SaaS environment.
- Demonstrate a strong track record leading multi-stakeholder projects that deliver platform features, scalable ML tooling, or end-end training systems.
- Exhibit a product-minded approach: comfortable partnering with product managers and data practitioners to balance trade-offs across usability, scalability, and complexity.
- Possess curiosity and adaptability to master new ML and data technologies, frameworks, and best practices.
- Communicate and collaborate effectively within remote and distributed teams.
