Senior Software Backend Engineer, Platform Computing

Iambic Therapeutics, Inc•San Diego, CA

1d•$168,000 - $210,000•Onsite

About The Position

We are seeking a Senior Software Backend Engineer, Platform Computing to integrate and operate the compute layer that powers data processing and model inference across Iambic’s drug discovery platform. You will work within the Software Platform team, partnering with ML engineers and scientists to ensure production workloads are reliable, performant, and scalable. Your focus will be on integrating and operating compute infrastructure and orchestration systems that enable scientific workflows across the platform. This position will be based at our San Diego Headquarters.

Requirements

10+ years of relevant experience with a Bachelor’s degree; or 8 years and a Master’s degree; or equivalent experience
Strong software engineering skills with proficiency in Python
Experience integrating and operating distributed or cloud-based systems in production
Familiarity with workflow orchestration frameworks (e.g., Prefect, Modal, Coiled, Airflow or similar)
Experience supporting data processing or compute-intensive workloads, including troubleshooting and performance optimization
Familiarity with containerization and deployment patterns (e.g., Docker, Kubernetes)
Solid understanding of cloud infrastructure (AWS preferred), including compute, networking, and storage
Ability to collaborate effectively with ML engineers to operationalize model outputs (e.g., working with PyTorch, Hugging Face, or similar frameworks at the integration level)
Demonstrates strong engineering rigor, including testing, version control, code quality, and observability practices

Nice To Haves

Experience working with data-intensive or scientific workflows is a plus

Responsibilities

Integrate and maintain the compute layer supporting data processing and model inference workflows in production
Ensure reliable and performant execution of workloads through monitoring, troubleshooting, and failure resolution
Operate and extend workflow orchestration systems (e.g., Prefect, Coiled, Modal or similar frameworks)
Support deployment and execution of ML model outputs in production environments
Enable scientific workflows by integrating external data sources and supporting analysis and inference workloads within and outside of Insight (e.g., large-scale assay refits)
Collaborate with ML engineers and cross-functional teams to translate model capabilities into production-ready systems
Integrate new compute capabilities into the platform as they are developed, ensuring scalability and flexibility
Establish and maintain best practices for reliability, observability, and operational support