Teams at Rockstar

Build and scale production ML infrastructure for distributed training, inference, fine-tuning, reinforcement learning, and GPU-heavy workloads. Develop cloud-native systems, pipelines, monitoring, logging, and observability tools while optimizing reliability, performance, and cost. Collaborate with ML scientists and customers to diagnose issues and ship backend, frontend, and infrastructure improvements. The role is suited to an early-career engineer working onsite with a fast-moving AI infrastructure startup.

Build the core intelligence layer for an AI-native clean energy infrastructure platform. Responsibilities include setting technical strategy, developing LLM-powered agent workflows, building backend features, creating data and geospatial pipelines, automating contracting and diligence, and shipping production software. The role requires autonomous execution, direct customer collaboration, rapid prototyping, technical leadership, and experience with GenAI systems, orchestration frameworks, vector databases, and backend engineering.

Own relationships with enterprise mortgage-lending customers after launch, driving adoption, success planning, renewals, expansion, account health, executive reviews, and issue resolution. Partner with Forward Deployed, Engineering, and Product teams to address escalations and influence the roadmap. Analyze call and usage data, use LLMs to automate customer-success workflows, and help establish scalable Customer Success playbooks.