
Full Stack Engineer
2 weeks ago
TeraCyte is looking for an experienced and self-driven
Senior Full Stack Cloud Engineer
to take end-to-end ownership of key software components that run across a highly distributed, data-intensive cloud environment. This role blends advanced backend and frontend engineering with responsibility for
designing cloud architecture focused on data storage, resource management, scalability, and performance
.
You'll work closely with imaging, AI, and infrastructure teams to ensure robust, efficient, and scalable pipelines, from raw data ingestion to user-facing visualization.
Responsibilities
- Design and implement scalable backend services using Python for data processing, image analysis, and algorithmic workflows.
- Own the architecture of cloud-native services with a focus on storage strategy, resource efficiency, and performance optimization.
- Develop and maintain modern, responsive frontend applications for visualizing and interacting with complex datasets.
- Collaborate with algorithm and data teams to integrate analytics and AI models into production environments.
- Build tools and services that enable traceability, reproducibility, and performance insights across the stack.
- Lead architectural decisions related to cloud environments, balancing cost, speed, and scalability.
Requirements
- 5+ years of experience in full stack or cloud engineering roles, preferably in data- or science-heavy environments.
- Deep understanding of cloud architecture, especially around storage management (e.g., blob storage, data lakes), compute resource optimization, and performance monitoring.
- Proficient in Python, with strong experience in building backend APIs and services.
- Solid understanding of image processing and computational pipelines.
- Skilled in modern frontend frameworks such as React, Vue, or Angular.
- Familiarity with distributed task frameworks (e.g., Celery) and messaging systems (e.g., RabbitMQ, Redis).
- Independent and self-motivated with the ability to lead technical initiatives across teams.
Nice to Have
- Experience working with high-throughput imaging systems or biomedical datasets.
- Exposure to MLOps or integration of AI/ML pipelines into production.
- Knowledge of cost/performance trade-offs in cloud environments (especially Azure).
If interested, please send email to
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