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ML Inference Engineer

Prime Intellect • United State
Remote Visa Sponsorship Relocation
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Build and optimize high-performance inference systems for RL research scaling to 1–3T parameter models. Responsibilities include improving rollout throughput, optimizing MoE inference, and reducing weight-update overhead. Requires deep expertise in GPU inference internals, Python, and PyTorch.

Key Highlights
Scaling inference systems for frontier-scale models (1-3T parameters) on NVL72 clusters
Optimizing rollout throughput for long-horizon agents and multi-node MoE inference
Reducing trainer-to-inference weight-update overhead through sharding-aware transfer
Key Responsibilities
Extend prime-rl’s vLLM integration to bring new model architectures into RL training.
Improve rollout throughput for long-horizon agents by tuning concurrency and KV-cache reuse.
Optimize multi-node MoE inference, including expert parallelism and prefill/decode separation.
Reduce trainer-to-inference weight-update overhead through sharding-aware transfer and communication overlap.
Build on FP8 and evaluate lower precision for inference.
Use trainer/sampler mismatch checks to catch numerical regressions.
Collaborate with model researchers on architecture choices to improve inference efficiency.
Technical Skills Required
Python PyTorch GPU Inference
Benefits & Perks
Cash Compensation Range of $150-350k
Equity incentives
Flexible work arrangements
Visa sponsorship
Relocation assistance
Quarterly team off-sites, hackathons, conferences and learning opportunities
Nice to Have
Contributions to vLLM or another inference engine
CUDA or Triton kernel work, particularly for low-precision inference

Job Description

Prime Intellect is building the open superintelligence stack: the infrastructure frontier AI labs build internally, made available to every ambitious AI team.
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