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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Platform and Infrastructure Engineer - **Company:** Gusto - **Location:** San Jose, CA, United States - **Experience:** Expert - **Salary:** $160,000.0 - $200,000.0 - **Contract:** Permanent contract - **Skills:** Java (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Automation of Tests, Data Infrastructure, Software Debugging, Python (Programming Language), Machine Learning, Ruby, Azure Machine Learning, Software Construction, Software Engineering, AI Infrastructure, Delivery Pipeline, Grafana, Build Management, Machine Learning Operations, Data Pipelines - **Published:** August 14, 2026 - **Apply:** https://www.dice.com/job-detail/2b146897-6959-4571-90e1-109bb0344996 ## About the Role * At least 5+ 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. ## Description 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. Here's what you'll do day-to-day: * 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 ## Related Videos - [Coffee with Developers: David Heinemeier Hansson](https://www.wearedevelopers.com/videos/875-coffee-with-developers-david-heinemeier-hansson) - [5 steps for running a Kubernetes environment at scale](https://www.wearedevelopers.com/videos/88-5-steps-for-running-a-kubernetes-environment-at-scale) - [Why and when should we consider Stream Processing frameworks in our solutions](https://www.wearedevelopers.com/videos/1085-why-and-when-should-we-consider-stream-processing-frameworks-in-our-solutions) - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) - [Coroutine explained yet again 60 years later](https://www.wearedevelopers.com/videos/690-coroutine-explained-yet-again-60-years-later) - [Developer Experience, Platform Engineering and AI powered Apps](https://www.wearedevelopers.com/videos/990-developer-experience-platform-engineering-and-ai-powered-apps) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Got AI ideas but no money? Here are 10 free ways to level up your AI skills with Google Cloud](https://www.wearedevelopers.com/magazine/600-got-ai-ideas-but-no-money-here-are-10-free-ways-to-level-up-your-ai-skills-with-google-cloud) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models)