ML Systems Engineer, Inference; Fully Remote

• Define how we measure inference performance, including throughput, time to first token, inter-token latency, and cost per token, and build tooling for rigorous, repeatable measurements.
• Profile and diagnose performance problems across the serving stack, from scheduling and memory management down to kernels and interconnect.
• Improve serving efficiency for large, state-of-the-art models on single-node and multi-node GPU deployments.

• 5+ years of professional system engineering experience with deep, hands-on experience in vLLM, SGLang, or a comparable serving engine in production.
• Strong software engineering skills in Python and a solid understanding of what drives LLM inference performance: batching, memory, parallelism, and latency/throughput trade-offs.
• Experience with modern inference optimization techniques such as quantization, speculative decoding, or distributed serving, plus rigor in benchmarking and GPU profiling.

• Competitive base pay from $150,000 - $220,000, meaningful equity, and generous medical, dental, and vision plans.
• Flexible PTO, a $1,200 home office and equipment stipend, and the opportunity to join a passionate team on the cutting edge of AI infrastructure.
• Most roles are remote-first with inclusive, collaborative teams using Slack for internal communication.

Runpod is the AI Developer Cloud, providing a platform for over one million developers to experiment, train, fine-tune, deploy, and scale AI. We're a small, remote-first team that takes ownership seriously, moves fast, and has processed more than 20 billion inference requests.