ML Systems Engineer — LLM Inference & Multi-Node Performance

ScOp Venture Capital LLC.; Santa Barbara, Northern (CA, KY); Hybrid; USD 150,000 - 210,000; Full time

Benefits offered by this job

Competitive compensation

Equity

Professional growth opportunities

Access to GPU compute resources

Job summary

ChipAgents seeks an ML Systems Engineer to optimize the performance and efficiency of large language model inference powering its agentic AI platform. This role centers on low-level systems optimization and building scalable multi-node clusters for training and inference that push LLM throughput and latency limits.

You will implement performance optimizations, build evaluation harnesses, and collaborate with researchers to integrate new model architectures into production infrastructure.

Qualifications

• 3+ years of experience with large-scale ML systems and GPU inference optimization.
• Strong Python and C++/CUDA skills; hands-on experience with SGLang, vLLM, PyTorch or similar frameworks.
• Deep understanding of GPU architecture, memory hierarchies, and parallel computing paradigms.
• Experience deploying and optimizing LLMs in production: model serving, batching, distributed inference, or quantization.
• Strong systems-level debugging and profiling skills; comfortable across CUDA kernels to application logic.
• Familiarity with distributed computing frameworks (Ray, multi-node training/inference) is a plus.
• Self-directed problem solver with a passion for ambitious optimization challenges.

Responsibilities

• Design, deploy, and optimize LLM inference systems across multi-node clusters, maximizing throughput and minimizing latency for production workloads.
• Implement and benchmark concrete inference optimizations.
• Profile and analyze inference bottlenecks from GPU kernel execution to memory bandwidth constraints.
• Build robust evaluation harnesses and benchmarking frameworks measuring accuracy, throughput, latency, and resource utilization across parallelism strategies.
• Collaborate with research scientists to integrate new model architectures and optimizations into production inference infrastructure.
• Investigate and apply emerging techniques from research papers and open-source projects to continuously improve inference performance.

Skills

Python

C++/CUDA

GPU architecture

ML systems

Profiling

Distributed computing

Model serving

SGLang

vLLM

PyTorch

CUDA kernels

Ray

Education

Bachelor/Master/PhD in CS/EE or related

Tools

SGLang

vLLM

PyTorch

Job description

ChipAgents seeks an ML Systems Engineer to optimize the performance and efficiency of large language model inference powering its agentic AI platform. This role centers on low-level systems optimization and building scalable multi-node clusters for training and inference that push LLM throughput and latency limits.

You will implement performance optimizations, build evaluation harnesses, and collaborate with researchers to integrate new model architectures into production infrastructure.