Senior ML Engineer - Fintech Infra & Production

Kikoff; San Francisco (CA); On-site; USD 244,000 - 292,000; Full time

Job summary

Kikoff is seeking a Senior Machine Learning Engineer to build and operate ML infrastructure for underwriting and other ML use cases. You will design pipelines, deploy models to production, and collaborate with cross-functional teams to align technical work with business goals.

Ideal candidates have 3+ years of ML engineering experience, strong Python or Ruby skills, and hands-on experience with ML frameworks, cloud platforms, and containerization.

Qualifications

• Bachelor's degree in CS, engineering, mathematics, or related field; advanced degree preferred.
• Minimum 3 years of experience in machine learning engineering with production deployments.
• Proficiency with ML frameworks: TensorFlow and PyTorch.
• Experience with cloud services (AWS, GCP) and containerization (Docker, Kubernetes).
• Proficient in Python or Ruby; strong software engineering practices.
• Excellent communication and collaboration skills.

Responsibilities

• ML Infrastructure and Operations: design, build, and maintain data pipelines and data loading for ML models.
• Model Development and Deployment: develop, deploy, and maintain ML models for underwriting and financial services.
• Collaborate with data scientists, software engineers, and PMs to deploy models in production.
• Monitor performance and optimize models for accuracy and efficiency.
• Design and run A/B tests to improve models and align with business goals.
• Mentor junior engineers and foster a learning culture.

Skills

Strong problem-solving

Excellent communication

Mentorship

Education

Bachelor's degree in a related field

Master's degree preferred

Tools

Python

Ruby

TensorFlow

PyTorch

AWS

GCP

Docker

Kubernetes

Job description

Kikoff is seeking a Senior Machine Learning Engineer to build and operate ML infrastructure for underwriting and other ML use cases. You will design pipelines, deploy models to production, and collaborate with cross-functional teams to align technical work with business goals.

Ideal candidates have 3+ years of ML engineering experience, strong Python or Ruby skills, and hands-on experience with ML frameworks, cloud platforms, and containerization.