Positron.ai
Skills: Python
About Positron AI
Positron AI is building next-generation AI inference accelerators designed from the ground up for low-latency, high-throughput large language model inference. Our first-generation ASIC, Asimov, is a cutting-edge accelerator targeting frontier AI workloads, with additional generations already underway.
Role Overview
This engineer will improve end-to-end large language model (LLM) inference performance on Positron's custom AI accelerators, including our FPGA-based Atlas systems and our Titan systems built on the Asimov ASIC. The work spans hardware-accelerated attention, accelerator tuning, distributed KV cache, and scalable inference serving architectures. It covers profiling, algorithms, GPU-side data preparation, host-to-device data movement, runtime behavior, and measurement on production-relevant models.
You will join a small, performance-focused team that is expected to optimize the whole stack, and not merely tune isolated kernel interfaces. The goal is to sustain a repeatable path from observing workload symptoms to identifying root causes and creating measured improvements.
Key Responsibilities
Performance Profiling and Optimization
Profile and optimize latency, throughput, utilization, and efficiency across hardware-accelerated attention, accelerator configuration, the device runtime, host software, and on-device execution. Use performance counters, hardware and software traces, telemetry, and simulation to isolate regressions and validate fixes.
Distributed Inference and KV Cache
Develop and optimize distributed inference capabilities, including efficient KV cache management and data movement across heterogeneous hardware.
Benchmarking and Test Infrastructure
Build representative benchmarks and workload traces that expose bottlenecks in compute, memory, chip-to-chip interconnect, networking, scheduling, and model loading. Partner with test engineers embedded in the team to turn performance findings into …truncated… a custom inference accelerator at a…
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