Staff Software Engineer, AI Inference

Syllo

1d•$190,000 - $230,000•Remote

About The Position

As we continue to scale our AI platform, we're investing in our own inference stack to deliver best-in-class performance, reliability, cost efficiency, and flexibility across the latest generation of open-source language models. We're looking for a Staff Software Engineer to spearhead this effort. You'll define the architecture, evaluate emerging technologies, and build the systems that power model serving. You'll partner closely with machine learning, infrastructure, and product engineering to establish the foundation for how AI models are deployed, optimized, monitored, and operated in production. This is a highly hands-on technical role. You'll spend the majority of your time designing, building, and optimizing production systems while helping shape our long-term AI infrastructure strategy.

Requirements

Significant experience designing and operating production AI inference systems.
Experience building or leading production LLM serving infrastructure.
Deep experience with one or more modern inference runtimes and frameworks such as vLLM, SGLang, TensorRT-LLM, Triton Inference Server, Hugging Face TGI, NVIDIA Dynamo, or comparable technologies.
Strong background in distributed systems, backend infrastructure, or high-performance platform engineering.
Experience optimizing inference performance across GPU workloads, including latency, throughput, batching, memory utilization, and serving efficiency.
Experience operating GPU infrastructure in production.
Strong proficiency in Python and at least one systems programming language (such as Go, Rust, or C++).
Proven ability to lead technical architecture for complex infrastructure initiatives.
Excellent communication skills and the ability to influence technical direction across engineering teams.

Responsibilities

Lead the design and development of our production inference platform.
Define the technical roadmap for inference infrastructure, model serving, and runtime optimization.
Build and operate scalable, cost-effective systems for serving large language models in production.
Evaluate and integrate modern inference technologies, frameworks, and serving runtimes.
Optimize latency, throughput, GPU utilization, memory efficiency, and infrastructure cost.
Develop systems for model deployment, traffic routing, autoscaling, scheduling, observability, and operational excellence.
Partner with ML engineers to productionize new models and inference techniques.
Establish benchmarking methodologies to evaluate new models, runtimes, and hardware.
Make key architectural decisions around when to build internally versus leverage open-source or commercial solutions.
Mentor engineers as the team grows and help establish engineering best practices for AI infrastructure.