2 months ago
Base Salary
$244k - $292k/yr
Responsibilities
- Design, build, and maintain ML infrastructure and data extraction, transformation, and loading pipelines.
- Develop and manage data pipelines and workflows for machine learning models.
- Design, develop, implement, and deploy scalable machine learning models for underwriting and financial service applications.
- Collaborate with data scientists, software engineers, product managers, and cross-functional teams to integrate models into production systems.
- Monitor deployed model performance and implement continuous improvement and optimization processes.
- Design and implement A/B tests and experiments to optimize models and align them with business goals.
- Mentor junior engineers and support a culture of learning and growth.
Requirements
- Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related field; an advanced degree is preferred.
- At least 3 years of machine learning engineering experience with a proven record of deploying ML models in production.
- Proficiency in Python or Ruby.
- Strong understanding of data structures, algorithms, and software design principles.
- Experience with machine learning frameworks and libraries such as TensorFlow and PyTorch.
- Familiarity with MLOps practices and tools for continuous integration and deployment of ML models.
- Experience with cloud services such as AWS or GCP and containerization technologies such as Docker and Kubernetes.
- Ability to analyze complex datasets, apply data science techniques, build predictive models, perform statistical analysis, and use machine learning algorithms.
- Strong verbal and written communication skills, including the ability to explain technical concepts to non-technical stakeholders.
Tech Stack
Categories
Data EngineeringML Engineering
About Kikoff
Kikoff builds consumer fintech products to help people establish and improve credit, including a reported Credit Account tradeline, rent and bill reporting, identity protection, and an AI-powered debt negotiation tool; its platform uses machine learning for underwriting, fraud, and personalization. The company monetizes through credit-building accounts and related financial services. Founded in 2019 and headquartered in San Francisco, it is privately held and serves U.S. consumers focused on credit building and financial wellness.