# Research Product Manager – AI Systems

**Company:** [Granica](https://hotfix.jobs/companies/granica)
**Location:** Mountain View, CA
**Role:** Product Management
**Salary:** $160k – $250k/yr
**Experience:** 5+ years
**Skills:** Machine Learning, ML Infrastructure, Model Evaluation, Benchmarking, Post-Training Systems, Structured Data Systems, Data Warehouses, Lakehouses, Distributed Systems, Performance Optimization
**Posted:** 2026-03-23

> Define and build AI evaluation, post-training, and structured data learning systems that improve model performance in production. Partner with researchers and engineers to translate experiments into scalable ML infrastructure.

## Job Description

## Responsibilities
- Define and drive systems for model evaluation, benchmarking, and real-world performance
- Build product direction for post-training systems and feedback loops that continuously improve models
- Define how models learn from large-scale structured and relational datasets
- Partner with engineering to build systems that connect data platforms (warehouses, lakehouses) with ML systems
- Own how improvements move from research experiments into production systems
- Model trade-offs across compute, data efficiency, performance, and cost
- Identify where system improvements drive measurable business impact

## Requirements
- 5+ years of experience in product management, technical program management, or similar roles in AI, ML infrastructure, or data systems
- Strong understanding of machine learning systems, including training, evaluation, and deployment
- Experience working with large-scale data systems or distributed infrastructure
- Ability to reason about trade-offs across data, compute, performance, and cost
- Track record of driving complex technical systems from concept to production

## Nice-to-Haves
- Experience with ML platforms, LLM systems, or AI infrastructure
- Experience with evaluation systems, observability, or model performance tooling
- Familiarity with structured or relational data systems (e.g., warehouses, lakehouses)
- Background in engineering, applied research, or ML systems development
- Experience operating in research-driven or highly ambiguous environments

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