Inference Systems Engineer for Scalable GPU ML (Remote)

Prime Intellect, Inc.; United States; A distancia; USD 180.000 - 240.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Equity incentives

Visa sponsorship

Relocation assistance

Flexible remote or in-person in SF

Descripción de la vacante

Prime Intellect in San Francisco is seeking a developer to build and optimize GPU inference systems powering RL research, scaling toward 1–3T parameter models on NVL72 clusters. The role aims to improve rollout throughput and enable frequent model updates.

You will extend vLLM integration for new model architectures, optimize multi-node MoE inference and transfer, and reduce trainer-to-inference weight update overhead.

Formación

• Experience building or optimizing GPU inference systems with understanding of inference-engine internals.
• Strong Python skills and experience with PyTorch or comparable ML frameworks.
• Ability to profile end-to-end workloads and turn measurements into verified performance improvements.

Responsabilidades

• Extend prime-rl’s vLLM integration for new model architectures with small-scale validation before larger runs.
• Optimize multi-node MoE inference, expert parallelism, and prefill/decode separation.
• Reduce trainer-to-inference weight-update overhead through sharding-aware transfer and communication overlap.
• Collaborate with researchers to implement deployment strategies.

Conocimientos

GPU inference systems

Python

PyTorch

Performance profiling

Descripción del empleo

Prime Intellect in San Francisco is seeking a developer to build and optimize GPU inference systems powering RL research, scaling toward 1–3T parameter models on NVL72 clusters. The role aims to improve rollout throughput and enable frequent model updates.

You will extend vLLM integration for new model architectures, optimize multi-node MoE inference and transfer, and reduce trainer-to-inference weight update overhead.