Upstart

About Upstart

Revolutionizing lending with AI-driven insights

🏢 Tech, Finance, Financial Services👥 1001+ employees📅 Founded 2012📍 South San Mateo, San Mateo, CA💰 $135.7m3.8
B2CMarketplaceLendingFinancial Services

Key Highlights

  • Raised $135.7 million in Series D funding
  • Over 100 banking partners, connecting millions of consumers
  • 90%+ loan automation rate, enhancing efficiency
  • Offers personal, auto, and home-equity loans

Upstart is an AI lending marketplace headquartered in South San Mateo, California, connecting millions of consumers with over 100 banks and credit unions. The company has raised $135.7 million in Series D funding and facilitates billions in loan originations by using advanced AI models to assess cre...

🎁 Benefits

Upstart offers comprehensive health plans, a 401k plan, generous vacation policy, flexible time off, parental leave, and family forming benefits throu...

🌟 Culture

Upstart fosters a culture focused on leveraging technology to democratize access to credit. With a strong emphasis on AI-driven solutions, the company...

Upstart

Staff Applied Machine Learning Engineer Senior

UpstartUnited States - Remote

Apply Now →

Overview

Upstart is seeking a Staff Applied Machine Learning Engineer to build LLM applications that enhance user experience in their AI lending marketplace. You'll work with technologies like Python and TensorFlow, focusing on machine learning and generative AI. This role requires significant experience in applied machine learning.

Job Description

Who you are

You have a strong background in applied machine learning, with at least 5 years of experience in developing and deploying machine learning models. Your expertise includes working with large language models (LLMs) and generative AI, and you are proficient in Python and popular ML frameworks like TensorFlow and PyTorch. You understand the intricacies of natural language processing (NLP) and have a passion for leveraging AI to solve real-world problems.

You thrive in cross-functional teams, collaborating closely with researchers, product managers, and engineers to bring innovative features to life. Your communication skills allow you to articulate complex technical concepts to non-technical stakeholders, ensuring alignment across teams. You are committed to inclusive and fair practices in your work and understand the importance of building AI solutions that are accessible to all.

Desirable

Experience with cloud platforms such as AWS or GCP is a plus, as is familiarity with data engineering practices. You may have contributed to open-source projects or published research in the field of machine learning, showcasing your commitment to the community and continuous learning.

What you'll do

As a Staff Applied Machine Learning Engineer at Upstart, you will be at the forefront of integrating machine learning into our lending products. Your primary responsibility will be to design and implement LLM applications that enhance the user experience and improve access to credit. You will work closely with a diverse team to identify opportunities for AI-driven features and lead the development process from ideation to deployment.

You will conduct experiments to validate your models and iterate based on user feedback and performance metrics. Your role will involve mentoring junior engineers and sharing your knowledge of best practices in machine learning and AI development. You will also collaborate with product teams to ensure that the AI solutions align with business goals and user needs.

What we offer

At Upstart, we offer a competitive salary and benefits package, including flexible working arrangements that allow you to work from anywhere in the United States. You will be part of a mission-driven company that is transforming the lending landscape through innovative technology. We foster a culture of collaboration and continuous improvement, encouraging you to grow your skills and advance your career within the organization. Join us in making a meaningful impact on access to affordable credit for all.

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