Work Location Type: Hybrid
Req Number 336181
About Grainger
W.W. Grainger, Inc. is a leading broad line distributor with operations primarily in North America and Japan. At Grainger, We Keep the World Working® by serving more than 4.6 million customers worldwide with maintenance, repair and operating (MRO) products and value-added solutions delivered through innovative technology and deep customer expertise. Known for its commitment to service and purpose-driven culture, the Company reported 2025 revenue of $17.9 billion. For more information, visit www.grainger.com.
Compensation
The anticipated base pay compensation range for this position is $134,100.00 – $223,500.00. This role is eligible for an incentive target of up to 15 %, based on the achievement of individual and company performance objectives in accordance with the current terms of the incentive program which are subject to change.
Position is not eligible for visa sponsorship.
Rewards and Benefits
With benefits starting on day one, our programs provide choice and flexibility to meet team members' individual needs, including:
- Medical, dental, vision, and life insurance plans with coverage starting on day one of employment and 6 free sessions each year with a licensed therapist to support your emotional wellbeing.
- 18 paid time off (PTO) days annually for full-time employees (accrual prorated based on employment start date) and 6 company holidays per year.
- 6% company contribution to a 401(k) Retirement Savings Plan each pay period, no employee contribution required.
- Employee discounts, tuition reimbursement, student loan refinancing and free access to financial counseling, education, and tools.
- Maternity support programs, nursing benefits, and up to 14 weeks paid leave for birth parents and up to 4 weeks paid leave for non-birth parents.
For additional information and details regarding Grainger’s benefits, please click on the link below:
https://experience100.ehr.com/grainger/Home/Tools-Resources/Key-Resources/New-Hire
Grainger Benefits
The pay range provided above is not a guarantee of compensation. The range reflects the potential base pay for this role at the time of this posting based on the job grade for this position. Individual base pay compensation will depend, in part, on factors such as geographic work location and relevant experience and skills.
The anticipated compensation range described above is subject to change and the compensation ultimately paid may be higher or lower than the range described above.
Grainger reserves the right to amend, modify, or terminate its compensation and benefit programs in its sole discretion at any time, consistent with applicable law.
Position Details
As a Lead Software Engineer (Software Engineer IV), you will develop applications that align with a strategic vision. In addition to coaching engineers, you will partner with key stakeholders including Product Managers and Architecture. You will embrace curiosity to ensure a deep understanding of the business requirements that drive the analysis and physical design of technical solutions.
Our 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. We build the platforms, patterns, services, and infrastructure that support a secure, scalable, and efficient AI/ML software development lifecycle.
In this role, you will work across machine learning, security, identity, infrastructure, architecture, and software engineering teams to define and build the foundational capabilities that power AI-enabled products and services across the enterprise. You will combine hands-on engineering with technical leadership, helping create self-service platforms, deployment frameworks, infrastructure automation, and emerging AI capabilities that accelerate innovation while maintaining reliability, security, and scalability.
This is an opportunity to influence the future of AI and machine learning at Grainger by building the foundations that allow teams to rapidly develop, deploy, monitor, and scale intelligent applications.
You Will
- Lead engineering efforts for highly complex software applications, ensuring the delivery of high-quality, maintainable, and scalable solutions.
- Influence team adoption of engineering best practices and sensible defaults that support continuous delivery and operational excellence.
- Contribute to a strong learning culture by mentoring engineers, leading knowledge-sharing sessions, conducting code reviews, and participating in pair programming.
- Maintain and support production systems, demonstrating the ability to triage, troubleshoot, and resolve complex issues.
- Partner directly with IT leadership, architects, product teams, and engineering stakeholders to ensure successful solution design and delivery.
- Design and build self-service AI/ML platform capabilities that streamline the machine learning lifecycle, including training, model versioning, validation, deployment, monitoring, and inference.
- Develop and enhance cloud-native platform services, infrastructure, and automation that support AI and machine learning workloads at scale.
- Partner with machine learning scientists, engineers, and application teams to understand workflows, identify friction points, and develop solutions that accelerate the 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 that guide 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 that improve deployment reliability, platform scalability, and engineering productivity.
You Have
- Bachelor's Degree or equivalent experience in computer science or a related discipline; or equivalent experience required.
- 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 multiple teams and stakeholder groups.
Preferred Qualifications
- 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.
We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex (including pregnancy), national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or expression, protected veteran status or any other protected characteristic under federal, state, or local law. We are proud to be an equal opportunity workplace.
We are committed to fostering an inclusive, accessible work environment that includes both providing reasonable accommodations to individuals with disabilities during the application and hiring process as well as throughout the course of one’s employment, should you need a reasonable accommodation during the application and selection process, including, but not limited to use of our website, any part of the application, interview or hiring process, please advise us so that we can provide appropriate assistance.
