Backend Software Engineer; ML Infra

Rockstar is recruiting for a mobile-first digital product studio that turns ideas into extraordinary experiences. They are a team of dynamic and savvy professionals who know how to create killer digital products. Our lean structure and remote team mean we can move fast while still delivering top-notch technology and design.

Our client is building the AI backbone for the next generation of intelligent products. They help fast-growing AI startups design, fine-tune, evaluate, deploy, and maintain specialized models across text, vision, and embeddings.

Think of them as “AWS for AI models”—not data or raw compute, but a full-stack backend for fine-tuning, reinforcement learning, inference, and long-term model maintenance.

Their customers are Series A–C AI companies building enterprise-grade products. Their promise is simple: they make your AI system better.

They are hiring a Backend Software Engineer (ML Infrastructure) to help design, build, and scale the core systems that power large-scale model training and deployment.

The candidate will work on distributed training pipelines, cloud-native infrastructure, and internal developer platforms that support fine-tuning, reinforcement learning, and inference s role sits at the intersection of backend engineering and ML systems—the candidate will collaborate closely with ML engineers while owning production-grade infrastructure.

This is an ideal role for an early-career engineer who wants to work on real distributed systems, GPU workloads, and modern ML infrastructure—not dashboards or CRUD apps.

• Design and implement backend systems that support large-scale ML workloads, including fine-tuning and reinforcement learning.
• Build distributed training and inference pipelines that are efficient, fault-tolerant, and observable.
• Develop internal developer tools and platforms that make it easier for ML engineers to train, evaluate, and deploy models.

• Work on cloud-native systems using containers and orchestration (e.g., Kubernetes).
• Optimize systems for performance, reliability, and cost efficiency, especially for GPU-heavy workloads.
• Implement monitoring, logging, and observability for long-running training jobs and production services.

• Partner closely with ML engineers to support evolving model architectures, training workflows, and evaluation needs.
• Translate ML requirements into scalable backend and infrastructure solutions.

• 1–3 years of backend engineering experience, ideally working on production systems.
• Strong fundamentals in distributed systems, networking, and backend architecture.
• Experience building systems that scale under real load.
• Comfortable working in Python and/or Go (or similar backend languages).
• Excited to work on-site in San Francisco with a fast-moving early-stage team.

• Experience with or exposure to ML infrastructure or ML platforms.
• Familiarity with GPU workloads, training pipelines, or inference systems.
• Experience with containerization and orchestration (Docker, Kubernetes).
• Contributions to or deep familiarity with ML infrastructure libraries such as:

• Ray
• vLLM
• SGLang
• or similar distributed ML systems

• Computer science background from a top-tier program or equivalent demonstrated excellence.
• Open-source contributions, research projects, or side projects in systems or ML infrastructure.
• A track record of high ownership and technical curiosity.