Technical Product Manager - Machine Learning Platform

Grainger Businesses•Chicago, IL

3d•$123,000 - $205,100•Hybrid

About The Position

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.

Requirements

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 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.
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.

Nice To Haves

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
Familiarity with modern MLOps and cloud-native platform concepts such as containers, Kubernetes, GitOps, CI/CD, workflow orchestration, observability, and self-service developer workflows.

Responsibilities

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.

Benefits

Medical, dental, vision, and life insurance plans with coverage starting on day one of employment
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)
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
Free access to financial counseling, education, and tools.
Maternity support programs
Nursing benefits
Up to 14 weeks paid leave for birth parents
Up to 4 weeks paid leave for non-birth parents.