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. •
Collaborate with cross-functional teams to understand business
Implement processes for continuous improvement and optimization of models. •
Requirements
Kikoff: A FinTech Unicorn Powering Financial Progress with AI
With innovative technology and AI, we simplify credit building, reduce debt, and expand access to financial opportunities to those who need them the most.
We value extreme ownership, clear communication, a strong sense of craftsmanship, and the desire to create lasting work and work relationships.
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.
Collaboration: Work closely with data scientists, software engineers, and product managers to integrate machine learning models into production systems.
Educational Background: Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related field. Advanced degree preferred. •
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 all look at data and pull it, and we believe that understanding the mechanics can yield valuable insights.
You absolutely need to be interested in data if you want to leverage your knowledge of systems. - Lucky .
Benefits
We're a profitable, high growth FinTech unicorn serving millions of people, many of whom are building credit or navigating life paycheck to paycheck.
Base Range $244,000 — $292,000 USD
Equal Employment Opportunity Statement
Additional details
At Kikoff, our mission is to provide radically affordable financial tools to help consumers achieve financial security.
Founded in 2019, Kikoff is headquartered in San Francisco and backed by top-tier VC investors and NBA star Stephen Curry. 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.
Yes, you can build an exciting business AND have real-life real-customer impact.
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.
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.
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.