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 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.
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