Technical Product Manager - Machine Learning Platform

Technical Product Manager - Machine Learning Platform Compensation The anticipated base pay compensation range for this position is $123,000.00 – $205,100.00. This role is eligible for an incentive target of up to 15 % or $ , 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. Work Eligibility This position is not eligible for any form of sponsorship now or in the future. Individuals requiring sponsorship (e.g. OPT or H1B visa status) should not apply. Only individuals authorized to work in the United States now and for the foreseeable future will be considered for this position. Rewards and Benefits 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: 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. The Technical Product Manager, ML Platform helps shape the vision, strategy, and roadmap for internal platform capabilities that enable Grainger’s Applied ML, ML Engineering, data science, analytics, and related technical teams to develop, deploy, monitor, and scale machine learning systems. This role works closely with ML Platform and ML Operations leadership to align user needs, platform health, delivery capacity, and business priorities in support of a reliable, self‑service ML platform that scales user impact and enables rapid innovation. The TPM partners with Product Engineering, Infrastructure, Security, Data Engineering, Applied ML, ML Engineering, and other technical stakeholders to improve platform usability, reliability, supportability, cost efficiency, and adoption across the machine learning development lifecycle. You Will Use data, customer feedback, and market signals to prioritize needs and features. Collaboratively drive the vision, strategy, and roadmap for internal technical products. Translate engineering needs into clear requirements and a prioritized backlog. Partner with engineering and infrastructure to deliver reliable, high‑quality software. Build strong relationships across teams in a matrixed organization. Use data to size opportunities, prioritize work, and track results. Partner with ML Platform and ML Operations leaders to inform roadmap priorities, clarify trade‑offs, and align platform investments to user needs, platform health, and team capacity. Lead structured discovery with Applied ML, ML Engineering, data science, analytics, and engineering users to identify platform needs and workflow friction. Bring user evidence, product analytics, support trends, and operational context into roadmap prioritization for initiatives that improve self‑service, ease of use, developer velocity, support experience, observability, reliability, and cost efficiency. Support awareness and adoption of ML platform capabilities through product messaging, documentation planning, demos, workshops, office hours, release communications, and feedback loops in partnership with ML Platform and ML Operations teams. You Have 5+ years experience working with software systems and program/project management 2+ years experience as a Technical Product Manager, Product Manager, Engineer, or Engineering Manager (or equivalent experience in a technical product or platform environment) 2+ years experience working in a cross‑functional, matrixed organization 2+ years experience assessing open‑source and vendor solutions, including time to market and total cost of ownership (preferred) 2+ years experience working with machine learning platforms, MLOps, data science platforms, developer platforms, infrastructure platforms, or internal technical products 2+ years experience translating complex technical workflows into product requirements, roadmaps, backlogs, and measurable success criteria 2+ years experience using product analytics, user research, support trends, stakeholder feedback, or operational metrics to prioritize internal platform improvements Ability to define a technical product vision and translate it into a roadmap for both technical and non‑technical audiences. Product judgment, including the ability to balance user value, technical feasibility, and business impact. Knowledge of engineering workflows, deployment practices, and system design fundamentals. Strong analytical and quantitative skills to support prioritization and sound decision‑making. Written and verbal communication skills, with the ability to influence without authority. Ability to navigate ambiguity and deliver results under tight timelines. Continuous improvement mindset and awareness of emerging technologies and industry trends. Understanding of the machine learning development lifecycle, including experimentation, deployment, serving, monitoring, support, and ongoing improvement of ML systems. Familiarity with modern MLOps and cloud‑native platform concepts such as containers, Kubernetes, GitOps, CI/CD, workflow orchestration, observability, and self‑service developer workflows. Ability to synthesize feedback from Applied ML, ML Engineering, data science, analytics, support channels, and platform usage data into actionable roadmap recommendations and prioritization inputs. Ability to evaluate platform opportunities across user impact, ease of use, technical feasibility, reliability, operational support, and cost efficiency. 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. #J-18808-Ljbffr W. W. Grainger