Lead Software Engineer - Machine Learning Platform Team

Grainger Businesses•Chicago, IL

2d•$134,100 - $223,500•Hybrid

About The Position

As a Lead Software Engineer (Software Engineer IV), you will develop applications aligned with a strategic vision, coach engineers, and partner with stakeholders including Product Managers and Architecture. You will understand business requirements and translate them into analysis and physical design of technical solutions. The AI/ML Platform Engineering team enables machine learning scientists and engineers to continuously develop, deploy, monitor, and improve machine learning models and agentic AI applications. The team builds platforms, patterns, services, and infrastructure for a secure, scalable, and efficient AI/ML software development lifecycle. You will work across machine learning, security, identity, infrastructure, architecture, and software engineering teams to build foundational capabilities for AI-enabled products and services across the enterprise. The role combines hands-on engineering and technical leadership to create self-service platforms, deployment frameworks, infrastructure automation, and emerging AI capabilities while maintaining reliability, security, and scalability.

Requirements

Bachelor's degree or equivalent experience in computer science or a related discipline.
At least 5 years of software engineering experience designing, developing, testing, and deploying scalable software applications across modern technology environments.
Expert-level programming knowledge and strong software engineering fundamentals.
Demonstrated knowledge of distributed system design, integration patterns, and cloud-native architectures.
Familiarity with Agile/Scrum methodologies and DevOps practices.
Experience analyzing, interpreting, and communicating complex technical challenges and solutions.
Experience building and supporting production software platforms using Python, Golang, or similar modern programming languages.
Experience with cloud platforms and services, preferably AWS, including services supporting containerized and distributed workloads.
Experience administering, operating, and troubleshooting Kubernetes environments in production.
Experience with infrastructure-as-code and deployment automation technologies such as Terraform, Helm, and Ansible.
Experience with CI/CD and GitOps tooling such as GitHub Actions, Argo CD, or similar technologies.
Experience supporting machine learning platforms, machine learning applications, or related frameworks and tooling.
Experience with application monitoring, observability, and reliability practices in cloud-native environments.
Ability to quickly learn unfamiliar technologies, evaluate potential solutions, and communicate clear technical recommendations.
Strong written and verbal communication skills, with the ability to influence technical decisions across teams and stakeholder groups.

Nice To Haves

Experience with identity and access management technologies and concepts, including OAuth 2.0, OpenID Connect, SAML, workload identity, role-based access control (RBAC), and policy-based authorization.
Experience with AI agents, agent frameworks, secure agent-to-system integrations, or Model Context Protocol (MCP).
Expertise designing, analyzing, and troubleshooting large-scale distributed systems.
Experience with big data technologies, distributed computing frameworks, or streaming platforms such as Spark, Kafka, Presto, or Flink.
Experience working within machine learning operations (MLOps), AI infrastructure, or platform engineering environments.

Responsibilities

Lead engineering efforts for highly complex software applications, ensuring delivery of high-quality, maintainable, and scalable solutions.
Influence adoption of engineering best practices and sensible defaults that support continuous delivery and operational excellence.
Mentor engineers, lead knowledge-sharing sessions, conduct code reviews, and participate in pair programming.
Maintain and support production systems; triage, troubleshoot, and resolve complex issues.
Partner with IT leadership, architects, product teams, and engineering stakeholders on solution design and delivery.
Design and build self-service AI/ML platform capabilities for the machine learning lifecycle, including training, model versioning, validation, deployment, monitoring, and inference.
Develop and enhance cloud-native platform services, infrastructure, and automation for AI and machine learning workloads at scale.
Partner with machine learning scientists, engineers, and application teams to understand workflows, identify friction points, and accelerate delivery of customer-facing capabilities.
Collaborate across machine learning, infrastructure, security, identity, architecture, and platform engineering teams to define reusable patterns, shared services, and enterprise standards.
Research and evaluate emerging technologies related to AI platforms, agentic AI, workload identity, authentication and authorization, Model Context Protocol (MCP), and distributed systems.
Develop proofs of concept, reference architectures, and implementation strategies for enterprise adoption of new platform capabilities.
Present technical findings, tradeoffs, risks, and recommendations to engineers, architects, security leaders, and senior technical stakeholders.
Drive DevOps and GitOps practices to improve deployment reliability, platform scalability, and engineering productivity.

Benefits

Medical, dental, vision, and life insurance coverage starting on the first day of employment.
Six free sessions each year with a licensed therapist.
18 annual paid time off (PTO) days for full-time employees, prorated based on employment start date.
Six company holidays per year.
6% company contribution to a 401(k) Retirement Savings Plan each pay period, with no employee contribution required.
Employee discounts.
Tuition reimbursement.
Student loan refinancing.
Free access to financial counseling, education, and tools.
Maternity support programs and nursing benefits.
Up to 14 weeks of paid leave for birth parents and up to 4 weeks of paid leave for non-birth parents.
Incentive target of up to 15%, based on individual and company performance objectives.