Serbia- AI Workloads Engineer

Description Next. Silicon is reimagining high-performance computing (HPC & AI). Our accelerated compute solutions leverage intelligent adaptive algorithms to vastly accelerate supercomputers, driving them forward into a new generation. We have developed a novel software-defined hardware architecture that is achieving significant advancements in both the HPC and AI domains.

At Next. Silicon, everything we do is guided by three core values: Professionalism : We strive for exceptional results through professionalism and unwavering dedication to quality and performance. Unity : Collaboration is key to success. That's why we foster a work environment where every employee can feel valued and heard.

Impact : We're passionate about developing technologies that make a meaningful impact on industries, communities, and individuals worldwide. The AI Workloads team is responsible for modeling and enabling end-to-end AI workflows on Next. Silicon’s next-generation hardware platforms. As an AI Workloads Engineer in Belgrade, you’ll build workflow modeling infrastructure, run and adapt open-source AI systems, and use real workloads to drive performance improvements from chip design through production.

Requirements

4+ years of experience in software engineering. Strong Python and Py. Torch development experience. Solid understanding of LLMs and modern inference workflows (e.g., KV cache, paged attention, speculative/assisted decoding, batching/scheduling) Experience running, profiling, and instrumenting open-source AI inference systems (e.

g., vLLM or similar) Proficiency in C++ for developing software that models or interacts with hardware execution behavior (latency, dataflow, memory access patterns). Experience with distributed inference and collectives (e.g., NCCL) and parallelism strategies (TP/PP/EP) is an advantage Experience with dynamic batching systems (e.

g., vLLM, TensorRT-LLM) is an advantage Familiarity with MLPerf Inference benchmarks and methodology (Server/Offline, latency constraints, request arrival patterns) is an advantage Experience programming custom kernels (e.g., CUDA, Triton, or similar) is an advantage Background in performance analysis, simulation, compiler/runtime profiling, or workload modeling is an advantage

Responsibilities

Model and analyze end-to-end AI workflows (e.g., assisted decoding, dynamic batching, dynamic KV cache, MLPerf-like scenarios) on Next. Silicon platforms, from simulation through production. Run and adapt open-source AI workloads, collecting and analyzing metrics such as latency, throughput, and traversal or arrival statistics.

Use SDK and framework-integration tools to profile full-stack behavior, identify performance bottlenecks, and drive improvements with compiler, runtime, and hardware design teams. Prototype custom kernels or runtime components when needed to enable or optimize new AI workflows on Next. Silicon hardware.