Software Engineer, Infrastructure
DatologyAIover 1 year ago
Base Salary
$180k - $300k/yr
Responsibilities
- Design and build development and production platforms that enable reliability and security at scale.
- Architect, build, and deploy core infrastructure across multiple cloud providers and deployment models.
- Develop infrastructure for model training and inference.
- Improve company productivity by providing engineers and teammates with effective tooling and systems.
- Partner with researchers and engineers to deliver new product features and research capabilities.
Requirements
- Meaningful experience spearheading and constructing large-scale infrastructure.
- Proficiency in Bash, Kubernetes, Python, and Terraform or similar technologies.
- Experience with AWS, other cloud platforms such as Azure or GCP, and/or on-premises environments.
- Expertise debugging issues across the stack, including networking, performance, hardware, and memory leaks.
- Experience building and operating scalable, reliable, and secure systems.
- Experience building data infrastructure from or nearly from scratch at a fast-growing startup is preferred.
- Experience building ML/DL infrastructure or data infrastructure that feeds into training large ML models is preferred.
- Ability to own problems end-to-end and learn missing knowledge as needed.
Benefits
- Role is based in Redwood City, California, with four days per week in the office.
- 100% covered medical, vision, and dental benefits.
- 401(k) plan with a 4% company match.
- Unlimited paid time off.
- Annual $2,000 wellness stipend.
- Annual $1,000 learning and development stipend.
- Daily lunches and snacks provided in the office.
- Relocation assistance for employees moving to the Bay Area.
Tech Stack
Categories
Data EngineeringDevOps
About DatologyAI
DatologyAI builds tools to automatically select the best data on which to train deep learning models. Our tools leverage cutting-edge research—much of which we perform ourselves—to identify redundant, noisy, or otherwise harmful data points. The algorithms that power our tools are modality-agnostic—they’re not limited to text or images—and don’t require labels, making them ideal for realizing the next generation of large deep learning models. Our products allow customers in nearly any vertical to train better models for cheaper.