Senior Machine Learning Engineer

About the role

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.

Responsibilities

• 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.
• Design, develop, and implement machine learning models for underwriting and other financial service applications. Ensure models are robust, scalable, and maintainable.
• 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.
• 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.
• Design and implement experiments to optimize models and ensure they align with business goals.
• Provide guidance and mentorship to junior engineers, fostering a culture of learning and growth within the team.

Requirements

• Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related field. Advanced degree preferred.
• Minimum of 3 years of experience in machine learning engineering, with a proven track record of deploying ML models in production environments.

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).
• 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.
• Excellent verbal and written communication skills, with the ability to convey complex technical concepts to non-technical stakeholders.

About us

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.

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.

We are 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.

We are 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.

We are 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.

We are 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.

Pay

Base range: $244,000 - $292,000 USD