You will join the Predictive Intelligence engineering team within Anaplan, building the backend services that power the ML Engine behind the Syrup platform and Anaplan’s forecasting solutions. The team is responsible for the production execution of forecasting and predictive models, the MLOps infrastructure supporting our data scientists, and the data processing services that deliver insights to enterprise customers. This role reports to the Director of Engineering for Predictive Intelligence and works closely with data scientists, ML engineers, and platform partners.

Your Impact

• Design and implement scalable, fault-tolerant predictive intelligence services and features as a core contributor to the ML Engine backend.
• 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.
• Drive the evolution of forecasting, scoring, and data processing services with a focus on performance, scalability, and cost efficiency.
• Take part in on-call rotations and own the operational health of high-availability production services, including incident response and post-incident improvements.
• Lead design reviews and code reviews, raising the quality bar across the team and mentoring mid-level and junior engineers.
• Identify and drive cross-cutting platform improvements that benefit multiple services and teams.

Your Qualifications

• 6+ years of professional software engineering experience building production backend services.
• Strong proficiency in Python, with a track record of writing performant, well-tested production code.
• Hands-on experience operating containerized services on Kubernetes in at least one major cloud (AWS, GCP, or Azure).
• Experience with data warehousing or analytics technologies such as Snowflake, Iceberg, Trino, or Postgres.
• Experience designing, deploying, and operating ML models in production, including familiarity with MLOps tooling such as MLflow.
• Demonstrated ability to work autonomously, take ownership of meaningful systems, and deliver against ambiguous requirements.
• Track record of being on-call for production services and contributing to operational excellence.
• Bachelor’s degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.

Preferred Skills

• 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.
• Experience working closely with data scientists to operationalize research code.
• Multi-cloud experience across AWS, GCP, and Azure.
• Background in forecasting, demand planning, or retail/supply chain domains.