Senior ML Engineer

Join Anaplan as a Senior ML Engineer, where you will be a core contributor to the ML Engine backend, responsible for building scalable and fault-tolerant predictive intelligence services. You will lead the technical implementation of MLOps capabilities, partner with data scientists to productionize models, and drive the evolution of forecasting and data processing services. You will also take part in on-call rotations, lead design and code reviews, and identify cross-cutting platform improvements.

Design and implement scalable, fault-tolerant predictive intelligence services and features as a core contributor to the ML Engine backend.

Responsibilities

• Lead the technical implementation of MLOps capabilities, including model training pipelines, deployment workflows, and runtime infrastructure for production ML.
• Partner with data scientists to productionize models and graduate experimental work into stable, observable production services.

Requirements

• Strong proficiency in Python, with a track record of writing performant, well-tested production code
• Experience with data warehousing or analytics technologies such as Snowflake, Iceberg, Trino, or Postgres
• Demonstrated ability to work autonomously, take ownership of meaningful systems, and deliver against ambiguous requirements
• 6+ years of professional software engineering experience building production backend services
• Experience designing, deploying, and operating ML models in production, including familiarity with MLOps tooling such as MLflow
• Bachelor’s degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience
• Hands-on experience operating containerized services on Kubernetes in at least one major cloud (AWS, GCP, or Azure)
• Track record of being on-call for production services and contributing to operational excellence
• Experience with gradient-boosted tree models, neural networks, and optimization solvers in production
• Familiarity with data orchestration tools (e.g., Prefect, Airflow, dbt) and modern data lake architectures
• Background in forecasting, demand planning, or retail/supply chain domains
• Multi-cloud experience across AWS, GCP, and Azure

Preferred

• Experience working closely with data scientists to operationalize research code