Product Manager (Specialized in Machine Learning)

Carter Support Services•Atlanta, GA

21h

About The Position

We are seeking a Product Manager with deep experience in machine learning–driven products to lead the strategy, development, and lifecycle of ML-powered solutions. This role sits at the intersection of business, data science, engineering, and user experience, translating complex machine learning capabilities into scalable, valuable, and user-friendly products. The ideal candidate understands both product management fundamentals and machine learning concepts , and can effectively guide teams through experimentation, model iteration, and production deployment while keeping a strong focus on customer outcomes and business impact.

Requirements

4+ years of product management experience, with at least 2 years working on machine learning or data-driven products
Strong understanding of machine learning concepts (e.g., supervised vs. unsupervised learning, model evaluation, training pipelines)
Experience working with data scientists, ML engineers, and software engineers
Ability to translate technical concepts into clear product requirements and user value
Experience defining success metrics and using data to drive product decisions
Excellent communication, stakeholder management, and prioritization skills

Nice To Haves

Experience launching ML products into production at scale
Familiarity with MLOps practices and model lifecycle management
Experience with cloud-based ML platforms (AWS, GCP, Azure)
Background in AI-driven products such as recommendations, forecasting, NLP, or computer vision
Knowledge of regulatory, ethical, and responsible AI considerations

Responsibilities

Define product vision, strategy, and roadmap for machine learning–based products
Translate business problems into ML product requirements and measurable success metrics
Partner closely with data science and engineering teams to guide model development, training, evaluation, and deployment
Own product discovery, including user research, hypothesis testing, and experimentation
Define and prioritize features using data, experimentation results, and business impact
Establish KPIs for ML products, including model performance, business outcomes, and user adoption
Manage product lifecycle from concept through launch, iteration, and scale
Communicate product strategy and progress to stakeholders and executive leadership
Ensure responsible AI practices, including fairness, transparency, and compliance considerations