ML Software Engineer: Build reusable ML Tools & APIs

Crowe; Costa Mesa (CA); On-site; USD 62,000 - 100,000; Full time

Job summary

Crowe in the United States is seeking a data science software engineer to help scale machine learning tools within the Toolkit Team. You will develop libraries and services in Python, collaborate with data scientists to move models from prototype to production, and champion reusable tooling across projects.

You will work in a Linux environment, use Docker for API delivery, and contribute to CI/CD pipelines, cloud deployments, and robust software engineering practices, shaping scalable solutions

Qualifications

• Programming experience with Python; familiarity with at least one other language is a plus.
• Software engineering experience with knowledge of algorithms and data structures.
• Comfortable in Linux environments; experience using Docker to develop and deliver RESTful APIs.
• Additional experience in cloud computing, distributed systems, CI/CD, etc.
• Ability to work autonomously and manage time between research and tool development.
• Strong communication skills for diverse audiences.

Responsibilities

• Execute tasks to achieve the project's technical vision throughout the development lifecycle.
• Support client intake conversations and data exploration for new projects; develop features and fix bugs in mature ML products.
• Maintain code quality and follow engineering best practices across projects.

Skills

Python

Docker

Linux

REST APIs

CI/CD

Cloud computing

Communication

Autonomy

Tools

Git

Job description

Crowe in the United States is seeking a data science software engineer to help scale machine learning tools within the Toolkit Team. You will develop libraries and services in Python, collaborate with data scientists to move models from prototype to production, and champion reusable tooling across projects.

You will work in a Linux environment, use Docker for API delivery, and contribute to CI/CD pipelines, cloud deployments, and robust software engineering practices, shaping scalable solutions