Software Engineer, Full-Stack
DatologyAI5 months ago
Base Salary
$180k - $300k/yr
Responsibilities
- Own the full product development lifecycle for customer-facing data curation products and new internal infrastructure and product experiences.
- Develop the core product for curating customer datasets and its visualizations, along with internal development tooling.
- Partner closely with the founders on product direction and business-critical technical decisions.
- Talk with customers and internal stakeholders to understand problems and design solutions.
- Collaborate with engineers, researchers, designers, and other cross-functional partners to deliver features and research capabilities.
- Ensure products and systems are reliable, secure, and worthy of customer trust.
Requirements
- Meaningful experience leading and building production full-stack and backend experiences for major product initiatives.
- Proficiency in JavaScript, TypeScript, React, other web technologies, and Python.
- Strong attention to product quality, functionality, design details, correctness, and testing.
- Ability to own problems end to end and learn the knowledge needed to complete the work.
- A humble, collaborative attitude and willingness to help the team succeed.
- Prior ML/AI experience is preferred but not required.
Benefits
- The 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
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.