Responsibilities
- Design systems for scheduling experiments, instruments, and computing resources across autonomous laboratory environments using durable Temporal workflows to manage long-running processes.
- Develop digital twin models by establishing a simulation and data infrastructure layer that enables teams to forecast factory operations prior to resource allocation.
- Lead technical data management by implementing Flyte pipelines, building an S3-based lakehouse, and designing PostgreSQL schemas to transform raw instrument output into governed, reliable datasets.
- Support data science initiatives by delivering batch and event-driven data pipelines that provide scientists and machine learning models with timely, high-quality data.
- Build customer-facing capacity planning services that allow teams to project and manage factory resource availability effectively.
- Ensure operational scalability by deploying and maintaining services on AWS EKS using Docker/ECR, Terraform for infrastructure, and GitHub Actions for CI/CD as the system expands.
Compensation
Competitive salary and equity package commensurate with experience.
Work Arrangement
Hybrid or remote with team coordination across time zones.
Team
Collaborative engineering team focused on robotics, automation, and data-intensive scientific workflows.
Technologies We Use
- Temporal for workflow orchestration
- Flyte for pipeline execution
- PostgreSQL for structured data modeling
- S3 for data lake storage
- AWS EKS for container orchestration
- Docker and ECR for container management
- Terraform for infrastructure as code
- GitHub Actions for CI/CD automation
Available for qualified candidates requiring sponsorship.
