Machine Learning Engineer II (Leasing) - AppFolio, Inc
San Diego, CA
About the Job
Hi, We’re AppFolio
We’re innovators, changemakers, and collaborators. We’re more than just a software company – we’re pioneers in cloud and AI who deliver magical experiences that make our customers’ lives easier. We’re revolutionizing how people do business in the real estate industry, and we want your ideas, enthusiasm, and passion to help us keep innovating.
We are hiring an ML Engineer II to join our AI Engineering Team, which is focused on solving real-world problems in Property Leasing. We work collaboratively to set the technical direction for our SaaS products, developing easy-to-use solutions for our customers. Our engineers find deep satisfaction in building things that customers actually need. We focus on delivering value to customers and understand that this often means delivering code that isn't perfect but meets customer needs.
This is an ideal opportunity for someone who has a passion for building leading-edge software and is driven to help build a successful SaaS product used by thousands of happy businesses. We foster an environment that empowers small teams to set the technical direction of our solutions collaboratively.
Your Impact
- Develop scalable, robust, and simple ML-powered solutions to solve complex business problems using Large Language Models (LLMs) and retrieval-based systems like RAG.
- Formulate, implement, and evaluate algorithms and database queries to support SaaS scalability, stability, and ML integration.
- Implement new features and optimize existing ones, leveraging Software Design principles and ML frameworks (e.g., PyTorch, LangGraph, Hugging Face) to drive maximum performance.
- Use test-driven development, code reviews, and continuous integration to deliver high-quality software while rapidly fixing bugs as they come up.
- Work closely with, and incorporate feedback from, engineering team members, QA, product owners, and our APM customers, particularly when integrating ML-driven features.
- Leverage agile practices to encourage collaboration, prioritization, and urgency to develop at a rapid pace.
- Research, share, and recommend new technologies and trends.
Qualifications
- You have experience working with a language like Python, Java, or similar object-oriented language.
- You have a solid understanding of machine learning concepts, including Large Language Models (LLMs), and are eager to deepen your knowledge. You are also familiar with classic machine learning algorithms (e.g., regression, classification, clustering) and how they integrate into larger systems.
- You love learning about new technologies but understand the value of using something tried-and-true if it meets your needs.
- You care about the long-term maintainability of the codebase and advocate for refactoring and clean-ups where appropriate, especially when implementing ML-based features.
- You care about work-life balance and want your company to care about it, too; you'll put in the extra hour when needed but won’t let it become a habit.
Must-Have
- Bachelor’s, Master’s, or Ph.D. in Computer Science, Machine Learning, or related technical discipline.
- Hands-on work experience developing ML-powered, web-based applications, preferably in a SaaS environment.
- Creativity, ability to solve complex problems without a roadmap.
Nice-to-Have
- Applied AI/ML Experience, particularly working with LLMs, RAG systems, or other conversational AI frameworks in a production system.
- Experience working across all levels of the development stack.
- Experience with some areas of our tech stack, like Ruby on Rails, React, Python, AWS, and SOA.
- Familiarity with Agile software development processes (Scrum or Kanban).
- Familiarity with Test-Driven Development (TDD).
Please note that base pay is one important aspect of a compelling Total Rewards package. The base pay range indicated here does not include any additional benefits or bonuses/commissions that you may be eligible for based on your role and/or employment type.
Regular full-time employees are eligible for benefits - see here.
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