About this role
The Predictive Intelligence engineering team at Anaplan builds the backend services that power the machine learning engine behind the Syrup platform and forecasting solutions. Engineers in this group focus on production execution for predictive models, MLOps infrastructure for data scientists, and data processing services for enterprise customers.
An experienced practitioner will design scalable predictive intelligence services, lead MLOps capability implementation, and partner with data scientists to productionize models. The person joining this group should operate autonomously, take ownership of high-availability production systems, and elevate team code quality through design reviews and mentoring.
What you will do
- Design and implement scalable, fault-tolerant predictive intelligence services and features as a core contributor to the machine learning engine backend.
- Lead the technical implementation of MLOps capabilities, including model training pipelines, deployment workflows, and runtime infrastructure for production machine learning.
- 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 to raise the quality bar across the team and mentor mid-level and junior engineers.
- Identify and drive cross-cutting platform improvements that benefit multiple services and teams.
What you need
- Bring 6+ years of professional software engineering experience building production backend services.
- Demonstrate strong proficiency in Python with a track record of writing performant and well-tested production code.
- Possess hands-on experience operating containerized services on Kubernetes within at least one major cloud environment such as AWS, GCP, or Azure.
- Have experience working with data warehousing or analytics technologies including Snowflake, Iceberg, Trino, or Postgres.
- Show experience designing, deploying, and operating machine learning models in production with familiarity in MLOps tooling like MLflow.
- Exhibit a demonstrated ability to work autonomously, take ownership of meaningful systems, and deliver against ambiguous requirements.
- Maintain a track record of participating in on-call rotations for production services and contributing to operational excellence.
- Hold a Bachelor's degree in Computer Science, Engineering, or a related technical field, or possess equivalent practical experience.
Nice to have
- Experience with gradient-boosted tree models, neural networks, and optimization solvers in production
- Familiarity with data orchestration tools 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
Skills and tech stack
Questions candidates ask
What team will I be joining?
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.
What kind of operational duties does the role involve?
You will take part in on-call rotations and own the operational health of high-availability production services, including incident response and post-incident improvements. A track record of being on-call for production services and contributing to operational excellence is required.
How does the hiring process work?
Anaplan does not extend offers to candidates without an extensive interview process with a member of our recruitment team and a hiring manager via video or in person. All offers are first extended verbally by a member of our internal recruitment team whenever possible and then followed up via written communication.
What technologies and tools are used in this role?
The tech stack includes Python, Kubernetes, major clouds like AWS, GCP, or Azure, and data warehousing or analytics technologies such as Snowflake, Iceberg, Trino, or Postgres. MLOps tooling includes MLflow, alongside potential familiarity with data orchestration tools and modern data lake architectures.