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# Full-Stack Software Engineer

- **Company:** ONE STOP COLLECTIBLE CORP
- **Location:** San Francisco, CA, United States (Remote available)
- **Experience:** Experienced
- **Salary:** $130,000.0 - $225,000.0
- **Contract:** Permanent contract
- **Skills:** Training Data, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Cloud Computing, Databases, Continuous Integration, Software Debugging, Programming Tools, Python (Programming Language), Machine Learning, Next.js, TypeScript, Reinforcement Learning, ReactJS, Grafana, Backend, Kubernetes, Terraform, Docker
- **Published:** October 22, 2026
- **Apply:** https://startup.jobs/full-stack-software-engineer-reinforcement-learning-clera-10532241

## About the Role

* At least 3 years of full-stack software engineering experience building and shipping production systems.
 * Hands-on experience with AI, machine learning, or reinforcement learning.
 * Proficiency in Python and a modern web stack such as React, TypeScript, or Next.js.
 * Experience owning user-facing or internal products end-to-end, from design through deployment.
 * Experience building backend services, APIs, and databases, plus practical knowledge of cloud infrastructure, Docker, CI/CD, and production debugging.
 * Background building dashboards, monitoring or observability interfaces, data inspection tools, review workflows, or quality assessment products.
 * Experience with data collection, labeling, annotation, or evaluation platforms is valuable. AWS, Kubernetes, Terraform, Grafana, developer tools, and partner-facing workflows are also relevant., On-site in San Francisco, California. Candidates able to maintain substantial time zone overlap with San Francisco or Singapore may also be considered for remote work.

## Description

Build the product interfaces, backend services, and internal tools that power an AI reinforcement learning data platform. You will work with research engineers and external partners to turn complex needs into useful products for creating and evaluating training data., * Develop tools for browsing environments, inspecting trajectories, reviewing task quality, and understanding model behavior.

 * Build partner workflows for creating, submitting, testing, and improving reinforcement learning environments and training data.
 * Create dashboards and observability tools for environment quality, evaluation results, data collection progress, and pipeline health.
 * Design backend services and APIs that connect task authoring, data collection, evaluation, quality review, and training infrastructure.
 * Collaborate with research, operations, and go-to-market teams to ship and improve systems.