Senior/Staff Software Engineer - Machine Learning Platform & Operations

Position Details: 

The Machine Learning Platform & Operations team is focused on enabling machine learning scientists and engineers at Grainger to continuously develop, deploy, monitor, and refine machine learning models as well as improving the ML software development process. Our mission is to empower Grainger teams to effortlessly build, ship, and scale reliable machine learning, data science, and analytical solutions by proactively listening to our users and anticipating Grainger’s evolving needs; delivering self-service, quality-first platforms that accelerate business outcomes. You will work with machine learning, data engineering, network, security, and platform engineering teams to build core components of a scalable, self-service machine learning platform that powers customer-facing applications. You will play an important part in developing the tools and services that form the backbone of Grainger’s AI driven features leveraging methods in Deep Learning, Natural Language Processing / Generative AI, Computer Vision, and beyond. This is an exciting opportunity to join a team fueling the next phase in Grainger Technology Group’s data- and AI-driven modernization. 

Our team is organized around three focus areas: 

• Machine Learning Operations & Infrastructure: Build and maintain core infrastructure components (i.e., Kubernetes clusters) and tooling enabling self-service development and deployment of a variety of applications leveraging GitOps practices. 

• Machine Learning Platform: Design and develop user-friendly software systems and interfaces supporting all stages of the machine learning development lifecycle. 

• Machine Learning Effectiveness & Enablement: Guide, partner, and consult with machine learning, product, and business domain teams from across the organization to foster responsible, scalable, and efficient development of high-quality ML systems. 

As such, we seek individuals with the ability to contribute to one or more of these focus areas. Successful candidates need not have prior hands-on experience with machine learning system development if they have relevant experience in other areas. Similarly, individuals with ML expertise and a solid understanding of software engineering best practices can be successful in this role without deep Kubernetes or DevOps knowledge, for example. While the details below are reflective of the skills present across our team, candidates need not possess them all. We are ultimately interested in speaking with curious individuals with an intuition for technical problem solving and are hiring across levels and focus areas. However, this is a software & platform engineering role, not a research/MLE role; you’ll code, design, and operate the platform that enables teams to build, ship, and scale. 

You Will: 

• Build self-service and automated components of the machine learning platform to enable the development, deployment, scaling, and monitoring of machine learning models. 

• Ship production platform components end-to-end across multiple modules; own reliability, performance, security, and cost from design through operation. 

• Design Helm releases and author GitOps objects (ArgoCD Applications/Projects) with RBAC/sync policies; keep deployments predictable and auditable. 

• Collaborate with machine learning, network, security, infrastructure, and platform engineers to ensure performant access to data, compute, and networked services. 

• Ensure a rigorous deployment process using DevOps standards and mentor users in software development best practices. 

• Partner with teams across the business to drive broader adoption of ML, enabling teams to improve the pace and quality of ML system development. 

You Have: 

• Bachelor’s degree and 5+ years’ relevant work experience or an equivalent combination of education and experience. 

• Track record building and operating production-grade, cloud-deployed systems (AWS preferred) with strong software engineering fundamentals (Python/Go or similar). 

• Expertise with IaC tools and patterns to provision, manage, and deploy applications to multiple environments using DevOps or GitOps best practices (e.g., Terraform/Helm + GitHub Actions/ArgoCD). 

• Familiarity with application monitoring and observability tools and integration patterns (e.g., Prometheus/Grafana, Splunk, DataDog, ELK). 

• Familiarity with containerization as well as container management and orchestration technologies (e.g., Docker, Kubernetes). 

• Ability to work collaboratively in a team environment.  

Bonus: 

• Expertise in designing, analyzing, and troubleshooting large-scale distributed systems and/or working with accelerated compute (e.g., GPUs). 

• Working knowledge of the machine learning lifecycle and experience working with machine learning systems and associated frameworks/tools, particularly for monitoring and observability. 

• Experience with big data technologies, distributed computing frameworks, and/or streaming data processing tools (e.g., Spark, Kafka, Presto, Flink). 

• Experience deploying, evaluating, and testing, or otherwise supporting, GenAI applications and their components (e.g., LLMs, Vector DBs, etc.). 

Don’t meet every single qualification? Studies show people are hesitant to apply if they don’t meet all requirements listed in a job posting. If you feel you don’t have all the desired experience, but it otherwise aligns with your background and you’re excited about this role, we encourage you to apply. You could be a great candidate for this or other roles on our team.