Low-Latency Inference Infrastructure Engineer

Elorian; Palo Alto, Northern (CA, KY); Hybrid; USD 200,000 - 400,000; Full time

Benefits offered by this job

Health, dental, and vision benefits

Unlimited PTO

Paid parental leave

Relocation support

Job summary

Elorian AI in Palo Alto is seeking an infrastructure engineer to design, optimize, and scale the systems that serve our large multimodal models. Your work will make inference faster, more cost-efficient, and more reliable, so our teams can focus on advancing model capabilities rather than managing bottlenecks.

Our focus is on performant, efficient inference, both to power real-world applications and to accelerate research.

Qualifications

• 3+ years of experience building low-latency, high-throughput inference serving systems for large models.
• Strong knowledge of inference optimization techniques (quantization, batching, speculative decoding, KV cache management).
• Hands-on experience with serving frameworks such as vLLM, TensorRT-LLM, Triton, or SGLang.
• Experience with multi-GPU/multi-node model parallelism for serving (tensor or pipeline parallel).
• Strong systems programming skills; C++/CUDA a plus alongside Python.
• Experience with autoscaling and load balancing for production ML services.
• A track record of GPU cost optimization at scale.

Responsibilities

• Build low-latency, high-throughput inference serving systems for our large multimodal models.
• Design and implement techniques that improve latency, throughput, and efficiency, including quantization, batching, speculative decoding, and KV cache management.
• Optimize our codebase and GPU fleet to fully utilize hardware FLOPs, bandwidth, and memory.
• Implement multi-GPU and multi-node model parallelism for serving (tensor or pipeline parallel).
• Build autoscaling and load balancing for production ML services.
• Establish standards for reliability, observability, and reproducibility across the inference stack.
• Collaborate with researchers to enable high-performance inference for novel architectures.

Skills

Low-latency inference

Inference optimization

Serving frameworks

Multi-GPU parallelism

Systems programming (C++/CUDA)

Autoscaling & load balancing

GPU cost optimization

Tools

vLLM

TensorRT-LLM

Triton

SGLang

Job description

Elorian AI in Palo Alto is seeking an infrastructure engineer to design, optimize, and scale the systems that serve our large multimodal models. Your work will make inference faster, more cost-efficient, and more reliable, so our teams can focus on advancing model capabilities rather than managing bottlenecks.

Our focus is on performant, efficient inference, both to power real-world applications and to accelerate research.