Senior Site Reliability Engineer
Heretic Stealth Portco
Updated on 3/25/2024
Heretic

11-50 employees

Founds companies using disruptive technology for cultural commerce
Company Overview
Heretic Ventures stands out as a dynamic workplace due to its unique positioning at the crossroads of culture, commerce, and creators, leveraging disruptive technologies such as AI for achieving widespread adoption and securing a lasting competitive edge. The company's culture is shaped by a forward-thinking ethos, as evidenced by its belief in defining the future through its ventures. Backed by industry-leading entrepreneurs and investors, Heretic offers an environment that encourages creativity, innovation, and industry leadership.
Venture Capital

Company Stage

Seed

Total Funding

$5M

Founded

2021

Headquarters

San Francisco, California

Growth & Insights
Headcount

6 month growth

6%

1 year growth

112%

2 year growth

41%
Locations
San Francisco, CA, USA
Experience Level
Entry
Junior
Mid
Senior
Expert
Desired Skills
Kubernetes
Python
Communications
Docker
Development Operations (DevOps)
Linux/Unix
Data Analysis
Google Cloud Platform
CategoriesNew
DevOps & Infrastructure
DevOps Engineering
Site Reliability Engineering
IT & Security
Cloud Engineering
Requirements
  • Bachelor's or Master's degree in Computer Science, a related field, or equivalent work experience
  • 5+ years of professional experience as DevOps, TechOps, or SRE engineer
  • Extensive experience with setting up IaaS cloud platforms (GCP preferred)
  • Experience scaling infrastructure for consumer facing web applications
  • Proven experience in working with and scaling GPUs
  • Proficiency in containerization technologies, especially Docker and Kubernetes
  • Proficient in Python and creating scripts to automate pipelines and processes
  • Extensive Linux troubleshooting experience
  • Excellent problem-solving and analytical thinking skills
  • Effective verbal and written communication
  • Comfortable working in a dynamic, fast-paced, and collaborative environment
Responsibilities
  • Build and extend tooling for end-to-end ML model deployment and lifecycle management
  • Setup, configure and connect cloud infrastructure services together to serve as the foundation of our platform
  • Automate deployment orchestration, building a fast and maintainable CI/CD pipeline for our web applications
  • Hook up real time monitoring and alerting for all parts of the web platform, enabling engineering teams to quickly respond to incidents
  • Build and maintain analytics pipeline, connecting data sources to data warehouse, then from data warehouse to reporting platform and back to model training
  • Collaborate with cross-functional teams to deploy and maintain AI models in production environments, ensuring scalability, reliability, efficiency and robustness
  • Orchestrate model serving to accommodate our unique infrastructure in a scalable manner
  • Configure and maintain Kubernetes clusters on Ubuntu
  • Maintain backend planning and optimize GPU capacity continuously