Systems Engineer (AI/ML Infrastructure)

Location: United States; remote or in-person at offices in San Francisco.

Salary: $150,000–$350,000 cash compensation range, including equity incentives.

Company

hirify.global builds open frontier AI infrastructure for compute, environments, evaluations, secure sandboxes, high-performance training, deployment, reinforcement learning, tool use, agent workflows, and continuously improving production models.

What you will do

• Build and optimize systems infrastructure for large-scale reinforcement learning and distributed training workloads through the prime-rl framework.
• Improve end-to-end training efficiency across compute, memory, networking, and scheduling layers.
• Design and implement low-level performance optimizations for kernels, communication paths, and runtimes.
• Develop distributed training systems using data, tensor, and pipeline parallelism.
• Shape the architecture of the reinforcement learning training stack, including asynchronous rollout and post-training systems.
• Contribute to open-source libraries and internal infrastructure while collaborating with researchers and infrastructure engineers on systems improvements.

Requirements

• Strong systems engineering experience in AI/ML infrastructure, particularly large-scale model training or inference.
• Deep familiarity with PyTorch and distributed training frameworks such as PyTorch Distributed, DeepSpeed, FSDP, Megatron, vLLM, Ray, or related tooling.
• Experience optimizing training performance across kernels, memory movement, communication overhead, or parallelization strategy.
• Hands-on experience with data parallelism, tensor parallelism, and pipeline parallelism.
• Strong understanding of GPU architecture, profiling, and performance debugging.
• Ability to identify bottlenecks across the stack and drive improvements from first principles.
• Comfort working in a fast-moving environment with ambiguous problems and high ownership.

Nice to have

• Experience writing or optimizing CUDA or Triton kernels.
• Experience with compiler or runtime optimization for ML systems.
• Experience with reinforcement learning training infrastructure, rollout systems, or asynchronous training pipelines.
• Experience with multi-node GPU clusters and high-performance networking.
• Contributions to open-source ML systems or infrastructure projects.
• Interest in publishing technical work or sharing insights through engineering blogs and technical writing.

Culture & Benefits

• Flexible work arrangements with the option to work remotely or in person in San Francisco.
• Visa sponsorship and relocation assistance for international candidates.
• Quarterly team off-sites, hackathons, conferences, and learning opportunities.
• Opportunity to work with a mission-driven team focused on accelerating science and AI.