Earnin
Staff Machine Learning Engineer (Applied ML)
Machine learning is integral to every financial service we provide. As we embark on a transformative phase, EarnIn is making significant investments to innovate and set new standards in ML applications within fintech. We are seeking skilled engineers to create groundbreaking solutions with large language models, generative AI, and advanced machine learning algorithms, generating substantial business and social impact. The Mountain View base salary range for this full-time position is $272,700 - $333,300 plus equity and benefits. Our salary ranges are determined by role, level, and location. This is a hybrid position in Mountain View and will require in-office work 2-3 days a week. What You'll Do
Design, develop, A/B test, and deploy risk models while collaborating with data scientists to drive data-driven decisions. Enhance credit and fraud models by incorporating innovative features on a quarterly basis and leveraging the latest industry research. Monitor feature and model health, and communicate changes in model decisions. Explore and integrate advanced technologies, including deep learning in the risk domain. Lead by example to foster operational excellence and transformative change. Expand responsibilities as new products emerge. What We're Looking For
Bachelor's or Master's degree in Computer Science, Engineering, or a related field. 7+ years of experience in machine learning with strong software engineering skills. Proficiency in a broad range of ML techniques deep learning, sequence models, and tree-based models. Advanced programming skills in Python and experience with ML frameworks such as TensorFlow or PyTorch. Hands-on experience with cloud-based ML platforms (e.g., AWS Sagemaker, Databricks, GCP Vertex AI). Hands-on experience with modern LLM stack: foundations models and APIs such as Open AI and Andtropic; LLM guardrails, framework such as LangGraph, LangChain; ML flow. Strong communication and collaboration skills. Passion for continuous learning and staying updated on industry trends.
Machine learning is integral to every financial service we provide. As we embark on a transformative phase, EarnIn is making significant investments to innovate and set new standards in ML applications within fintech. We are seeking skilled engineers to create groundbreaking solutions with large language models, generative AI, and advanced machine learning algorithms, generating substantial business and social impact. The Mountain View base salary range for this full-time position is $272,700 - $333,300 plus equity and benefits. Our salary ranges are determined by role, level, and location. This is a hybrid position in Mountain View and will require in-office work 2-3 days a week. What You'll Do
Design, develop, A/B test, and deploy risk models while collaborating with data scientists to drive data-driven decisions. Enhance credit and fraud models by incorporating innovative features on a quarterly basis and leveraging the latest industry research. Monitor feature and model health, and communicate changes in model decisions. Explore and integrate advanced technologies, including deep learning in the risk domain. Lead by example to foster operational excellence and transformative change. Expand responsibilities as new products emerge. What We're Looking For
Bachelor's or Master's degree in Computer Science, Engineering, or a related field. 7+ years of experience in machine learning with strong software engineering skills. Proficiency in a broad range of ML techniques deep learning, sequence models, and tree-based models. Advanced programming skills in Python and experience with ML frameworks such as TensorFlow or PyTorch. Hands-on experience with cloud-based ML platforms (e.g., AWS Sagemaker, Databricks, GCP Vertex AI). Hands-on experience with modern LLM stack: foundations models and APIs such as Open AI and Andtropic; LLM guardrails, framework such as LangGraph, LangChain; ML flow. Strong communication and collaboration skills. Passion for continuous learning and staying updated on industry trends.