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You will have ownership over designing, building, and scaling the core infrastructure that brings Prior Labs' foundation models to the world. This is a unique opportunity to make fundamental architectural decisions, establish engineering best practices from the ground up, and profoundly shape the technical direction for serving state-of-the-art AI. You'll work directly with world-class AI researchers, translating cutting-edge models into reliable, scalable production systems. This role offers significant autonomy and impact, with clear paths to specialize in areas you're passionate about (like ML infrastructure or core backend systems) or grow into a technical leadership position as our team expands. You won't just be implementing features; you'll be building the backbone of our company.
Job Responsibility:
Architect & Design: Design robust, scalable, and secure backend systems and production-grade APIs for serving and finetuning our foundation models
Own Infrastructure: Design, deploy, and manage core infrastructure on cloud platforms, focusing on reliability, monitoring, observability, and cost-efficiency
Core MLOps Concepts: Understanding of the entire machine learning lifecycle (MLLC) from data ingestion and preparation to model deployment, monitoring, and retraining
Ensure Compliance & Security: Implement secure, GDPR-compliant systems, including data storage, access control, usage tracking, and quota management
Champion Best Practices: Drive high standards for testing, CI/CD, documentation, and security within the engineering team
Requirements:
3+ years of professional experience in a cloud engineering, data platform, or SRE role, with a proven track record of managing production infrastructure
Proven experience building and maintaining data-intensive systems, with a strong understanding of data modeling, storage, and processing technologies
Strong, hands-on experience with Infrastructure as Code (IaC) using tools like Terraform
Significant experience with containerization and orchestration technologies (Docker, Kubernetes)
Proficiency in Python
Nice to have:
Experience building or managing infrastructure specifically for machine learning (MLOps, model serving frameworks, feature stores, data pipelines)
Hands-on experience with modern data warehousing and processing platforms like Databricks, Snowflake, or BigQuery
Experience in a technical leadership or mentorship role
Contributions to relevant open-source projects
What we offer:
Competitive compensation package with meaningful equity
30 days of paid vacation + public holidays
Comprehensive benefits including healthcare, transportation, and fitness
Work with state-of-the-art ML architecture, substantial compute resources and with a world-class team