Backend Software Engineer (ML Infra)

Rockstar is recruiting for a fast-growing startup that 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 at scale. This 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.What You’ll DoBuild & Scale Core Infrastructure- 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.Cloud & Systems Engineering- 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.Collaborate with ML Engineers- Partner closely with ML engineers to support evolving model architectures, training workflows, and evaluation needs.- Translate ML requirements into scalable backend and infrastructure solutions.Who You AreRequired- 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.Strongly Preferred- 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 systemsBonus- 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.

Rockstar is recruiting for a fast-growing startup that 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 at scale. This 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.

What You’ll Do

Build & Scale Core Infrastructure

- 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.

Cloud & Systems Engineering

- 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.

Collaborate with ML Engineers

- Partner closely with ML engineers to support evolving model architectures, training workflows, and evaluation needs.

- Translate ML requirements into scalable backend and infrastructure solutions.

Who You Are

Required

- 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.

Strongly Preferred

- 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

Bonus

- 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.