# Early Career Software Engineer – Applied AI

**Company:** [Wonderschool](https://hotfix.jobs/companies/wonderschool)
**Location:** San Francisco, CA
**Role:** ML Engineering
**Salary:** $100k – $120k/yr
**Experience:** 0+ years
**Skills:** Python, JavaScript, TypeScript, TensorFlow, PyTorch, scikit-learn, GCP, AWS, LangChain, Rasa
**Posted:** 2026-04-28

> Early career software engineer builds and integrates AI agents and solutions using frameworks like LangChain and RAG pipelines to enhance childcare platform features. Requires bachelor's in CS/engineering, Python/JS proficiency, and AI/ML familiarity; hybrid onsite in SF office 3 days/week.

## Job Description

## Key Responsibilities

- Design, develop, and maintain robust software solutions with a focus on integrating AI capabilities.
- Collaborate with product managers, designers, and engineers to define requirements and implement innovative features.
- Design and Development of AI Agents: Build modular, task-specific AI agents capable of natural language understanding, dialogue management, and action execution using frameworks such as **LangChain** or **Rasa**.
- Debug and troubleshoot technical issues to ensure platform stability.
- Stay current with emerging technologies and best practices in software engineering and AI.

## Required Qualifications

- **Bachelor’s degree** in Computer Science, Engineering, or related field.
- Strong foundation in programming languages (**Python**, **JavaScript**, or **TypeScript**).
- Familiarity with **AI/ML** concepts and frameworks (**TensorFlow**, **PyTorch**, **Scikit-learn**).
- Basic understanding of cloud platforms like **Google Cloud Platform** and **AWS**.
- Excellent communication skills and ability to work in a collaborative, in-person environment.

## Nice-to-Have Skills

- Strong understanding of machine learning principles, including model development, evaluation, and deployment for real-world applications.
- **Retrieval-Augmented Generation (RAG)** Pipelines: Design and deploy RAG pipelines by integrating retrievers with generative models to enable contextual, real-time responses using vectorized knowledge bases.
- **Continuous Learning**: Incorporate user feedback loops and data augmentation techniques to iteratively improve the performance and relevance of AI agents and RAG pipelines.

## Compensation

- Expected salary range: **$100K–$120K** annually, based on experience and location.

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