Software Engineer, ML Platform

Gusto, Inc.•Quinte West, ON

1d•$160,000 - $240,000•Hybrid

About The Position

Gusto is looking for a strong Machine Learning Platform and Infrastructure Engineer to join our ML Platform team and build out and scale our ML and AI platform. As a Machine Learning Platform Engineer, you will work closely with AI/ML engineers to rapidly build, deploy, and iterate high-quality ML/AI infrastructure solutions at scale, ensuring both reliability and effectiveness. Your deep expertise in the machine learning model development cycle, along with a strong understanding of data pipelines and data infrastructure will be crucial in developing a dependable and scalable ML/AI infrastructure for all of Gusto to rely on. The ideal candidate is passionate about developing software, developing and documenting optimal processes, working with data, and understanding the needs of end users. A strong grasp of ML and data infrastructure is essential, as you will work with stakeholders to build efficient solutions to help our partners scale x times better.

Requirements

At least 4+ years of software engineering experience (Python, Ruby or Java).
Demonstrated experience designing and developing infrastructure and platform services for machine learning lifecycle, such as feature stores, model development, deployment, and observability tools and solutions.
Experience with at least one of the major cloud platforms (AWS preferred but not required).
Curiosity and experimentation with emerging AI frameworks, applying and sharing best practices to evaluate and scale AI use safely across teams
Comfort with AI-assisted development tools and a habit of staying current with emerging approaches to building software.

Responsibilities

Build core components of our ML and AI Platform technical roadmap to design and build MLOps solutions with automated pipelines and standardized processes to build, deploy, run, monitor, debug, and retrain ML and AI Models.
Develop, maintain, and enhance frameworks for machine learning model development and deployment.
Collaborate with the ML/AI builders and application owners to determine business requirements and SLAs for API-enabled services.
Develop, maintain, and enhance infrastructure supporting machine learning services.
Support the development of new patterns for the deployment of machine learning models with CI/CD pipelines and automated testing.
Apply AI tools as a regular part of your engineering workflow, and bring an AI-native lens to engineering and product decisions: identify where AI can reduce effort, simplify complex workflows, and surface proactive guidance.
Adopt the latest best practices for using AI technologies across all aspects of technical development