5 hours ago
San Francisco, CA, USA or New York, NY, USAMid Level
H1B sponsor
Base Salary
$180k - $225k/yr
Responsibilities
- Design and implement foundational platforms and systems in collaboration with stakeholders and internal customers.
- Define, design, and deliver new features with cross-functional teams.
- Improve programming practices through process enhancements and tooling upgrades.
- Present technical information and provide guidance on development processes and technologies.
- Support foundational data platforms, cloud infrastructure, orchestration systems, and software development, build, testing, and deployment workflows.
Requirements
- 3+ years of full-time engineering experience post-graduation, specializing in backend systems.
- Extensive software development experience and deep knowledge of distributed systems and public cloud platforms, preferably AWS.
- Demonstrated independent ownership of successful engineering projects.
- Strong communication and collaboration skills, including the ability to explain complex technical concepts to non-technical stakeholders.
- Experience with Kubernetes, Terraform, Docker, Temporal, AWS Step Functions, MongoDB, Postgres, and CircleCI.
- Preferred experience with Snowflake, Firebolt, Dagster, dbt, Zanzibar, or other authentication and authorization systems.
- Preferred experience scaling products at hyper-growth startups and interest in AI technologies.
Benefits
- Comprehensive health, dental, and vision coverage.
- Retirement benefits, learning and development stipend, and generous paid time off.
- Potential commuter stipend and equity-based compensation for eligible roles.
- The listed base salary range for the San Francisco position is $180,000–$225,000 per year.
- The role is full-time and based in San Francisco.
Tech Stack
About Scale AI
Scale AI builds data annotation services and AI development tools for enterprises and government agencies, sold as a platform and managed services. Its products include the Scale Generative AI Platform for building and evaluating agents and the Data Engine for collecting, curating, and labeling training data, including RLHF and model evaluation. Founded in 2016 and headquartered in San Francisco, the company is privately held and works across domains from computer vision to LLM applications.