Job description

Kikoff: The Fintech Powering Financial Security at Scale Kikoff is a profitable, pre-IPO fintech company on a mission to empower everyone to achieve financial security. With record revenue growth in 2025 and a unicorn valuation, we've built a suite of products that help millions of people build credit, access liquidity, and save money. We're scaling fast. Join us if you want to build something meaningful and help millions of people move forward financially. Why Kikoff: This is a consumer fintech startup, and you will be working with serial entrepreneurs who have built strong consumer brands and innovative products. We value extreme ownership, clear communication, a strong sense of craftsmanship, and the desire to create lasting work and work relationships. Yes, you can build an exciting business AND have real-life real-customer impact. We are seeking a Senior Machine Learning Engineer to join our team. This role will focus 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 in deploying scalable ML models in production environments. Key Responsibilities: ML Infrastructure and Operations: Design, build and maintain the infrastructure required for optimal extraction, transformation, and loading of data 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 service applications. Ensure models are robust, scalable, and maintainable. Collaboration: Work closely with data scientists, software engineers, and product managers to integrate machine learning models into production systems. Collaborate with cross-functional teams to understand business requirements and translate them into technical solutions. Performance Monitoring: Monitor and evaluate the performance of deployed models, ensuring they meet the desired accuracy and efficiency metrics. Implement processes for continuous improvement and optimization of models. A/B Testing and Experimentation : Design and implement experiments to optimize models and ensure they align 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. Advanced degree preferred. Experience: Minimum of 3 years of experience in machine learning engineering, with a proven track record of deploying ML models in production environments. Technical Skills: Proficiency in programming languages such as Python or Ruby. Strong understanding of data structures, algorithms, and software design principles. Experience with machine learning frameworks and libraries (e.g., TensorFlow, PyTorch). Familiarity with MLOps practices and tools for continuous integration and deployment of ML models. Experience with cloud services (e.g., AWS, GCP) and containerization technologies (e.g., Docker, Kubernetes). Analytical Skills: Strong problem-solving skills with the ability to analyze complex data sets, apply advanced data science techniques, and derive actionable insights. Proficient in building predictive models, performing statistical analysis, and utilizing machine learning algorithms to identify trends, patterns, and opportunities for optimization. Communication Skills: Excellent verbal and written communication skills, with the ability to convey complex technical concepts to non-technical stakeholders. What we’re like: - Scrappy . We had a product goal and put out the MVP, collecting our first users with steady growth via paid channels in four months. We don’t cut corners when we know we’ll need them but we don’t build things without that need. We don’t like inefficiency but we dislike operationalizing one-off tasks even more. - Risk-oriented . Everything has risk, but a mature team knows how to make these tradeoffs. That’s why we built the MVP fast––because time is your most valuable asset and is practically fungible with money in the startup world. - Data-obsessed . We all look at data and pull it, and we believe that understanding the mechanics can yield valuable insights. Complex systems require elegant, not just simple solutions. You absolutely need to be interested in data if you want to leverage your knowledge of systems. - Lucky . That’s how we look at this journey so far. From our timing of fundraising, to the circumstances in which we came together, to the initial product traction we’re getting, there’s no other word to describe it. We are grateful you are reading this, and we know that if you’re meant to be with us on this journey, then we will see you soon. Base Range $257,000 — $312,000 USD A note on applying: We know great candidates sometimes hold back if they don’t meet every qualification listed. If you’re excited about this role and believe you can do the work, we encourage you to apply. We’re looking for capability and trajectory, not a perfect checklist match. Equal Employment Opportunity Statement Kikoff Inc. is an equal opportunity employer. We are committed to complying with all federal, state, and local laws providing equal employment opportunities and considers qualified applicants without regard to race, color, religion, creed, gender, national origin, age, disability, veteran status, marital status, pregnancy, sex, gender expression or identity, sexual orientation, citizenship, or any other legally protected class. Please reference the following for more information .

Requirements

Must-haves

Model Development and Deployment: Design, develop, and implement machine learning models for underwriting and other financial service applications. Ensure models are robust, scalable, and maintainable.
Performance Monitoring: Monitor and evaluate the performance of deployed models, ensuring they meet the desired accuracy and efficiency metrics. Implement processes for continuous improvement and optimization of models.
A/B Testing and Experimentation : Design and implement experiments to optimize models and ensure they align with business goals.
Mentorship: Provide guidance and mentorship to junior engineers, fostering a culture of learning and growth within the team.
Experience: Minimum of 3 years of experience in machine learning engineering, with a proven track record of deploying ML models in production environments.
Proficiency in programming languages such as Python or Ruby.
Strong understanding of data structures, algorithms, and software design principles.
Experience with machine learning frameworks and libraries (e.g., TensorFlow, PyTorch).
Familiarity with MLOps practices and tools for continuous integration and deployment of ML models.
Experience with cloud services (e.g., AWS, GCP) and containerization technologies (e.g., Docker, Kubernetes).
Communication Skills: Excellent verbal and written communication skills, with the ability to convey complex technical concepts to non-technical stakeholders.

Nice-to-haves

Educational Background: Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related field. Advanced degree preferred.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningmlpythonawsgcpdockerkubernetes

Platform: greenhouse
Effort: quick