Member of Technical Staff - Machine Learning Join us to apply for the Member of Technical Staff - Machine Learning role at Kikoff .

Are you ready to apply Make sure you understand all the responsibilities and tasks associated with this role before proceeding. We are seeking a Senior Machine Learning Engineer to join our team. This role focuses on developing and maintaining machine learning infrastructure and operations, particularly for our cash advance underwriting model and other machine learning use cases. The ideal candidate will have a strong background in software development, machine learning, and data engineering, with experience deploying scalable ML models in production environments. Key Responsibilities ML Infrastructure and Operations: Design, build, and maintain infrastructure for data extraction, transformation, and loading from various sources. Develop and manage data pipelines and workflows for machine learning models. Model Development and Deployment: Design, develop, and implement machine learning models for underwriting and other financial applications. Ensure models are robust, scalable, and maintainable. Collaboration: Work closely with data scientists, software engineers, and product managers to integrate ML models into production systems. Collaborate with cross-functional teams to understand business requirements and translate them into technical solutions. Performance Monitoring: Monitor and evaluate deployed models' performance, ensuring they meet accuracy and efficiency metrics. Implement processes for continuous improvement and optimization. A/B Testing and Experimentation: Design and implement experiments to optimize models and align them with business goals. Mentorship: Provide guidance and mentorship to junior engineers, fostering a culture of learning and growth within the team. Qualifications Educational Background: Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related field. An advanced degree is preferred. Experience: Minimum of 3 years in machine learning engineering, with a proven track record of deploying ML models in production. Technical Skills: Proficiency in Python or Ruby; strong understanding of data structures, algorithms, and software design; experience with frameworks like TensorFlow or PyTorch; familiarity with MLOps tools; experience with cloud services (AWS, GCP) and containerization (Docker, Kubernetes). Analytical Skills: Strong problem-solving skills, ability to analyze complex data, apply data science techniques, and derive insights. Experience building predictive models and performing statistical analysis. Communication Skills: Excellent verbal and written communication, capable of conveying technical concepts to non-technical stakeholders. What We’re Like Scrappy: Fast-paced, focused on delivering MVPs, avoiding inefficiency, and operationalizing scalable solutions. Risk-oriented: Balancing risk and reward, making informed tradeoffs to accelerate progress. Data-obsessed: Passionate about data, understanding system mechanics, and leveraging insights for optimization. Lucky: Appreciative of our journey and the serendipity involved, welcoming talent to join us. Kikoff: A FinTech Unicorn Powering Financial Progress with AI Our mission is to provide affordable financial tools to help consumers achieve security. As a profitable, high-growth FinTech unicorn, we serve millions, helping build credit, reduce debt, and expand financial access through innovative AI technology. Founded in 2019, based in San Francisco, backed by top-tier investors and Stephen Curry. Why Kikoff Work with serial entrepreneurs, own your work, communicate clearly, and create lasting impact. We offer comprehensive benefits, including health coverage, stock options, unlimited vacation, and visa sponsorship for exceptional talent. Equal Opportunity Kikoff is committed to equal employment opportunity and considers all qualified applicants regardless of race, color, religion, gender, or other protected classes. For accommodations, contact talent@kikoff.com. San Francisco Fair Chance Ordinance: We consider qualified applicants with arrest and conviction records.

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