Distributed Systems Engineer, Data & Inference Platform

Adaption is a company focused on building efficient intelligence that evolves in real-time. They are seeking a Distributed Systems Engineer to build and operate systems for LLMs and data pipelines, ensuring reliability and efficiency in production environments.

Responsibilities:

• Serve Models at Scale: Design and operate distributed inference systems for LLMs, optimizing throughput, latency, and cost across heterogeneous GPU fleets. Batching, scheduling, KV cache management, autoscaling — you own the levers that make inference economical
• Move the Data: Build large-scale data pipelines (Ray Data, Spark, or equivalents) that ingest, transform, and curate the datasets behind training and evaluation. The bottleneck is rarely where people think it is, and you find it
• Debug the Undebuggable: Chase down the failure modes that only emerge under real production traffic — stragglers, head-of-line blocking, silent data corruption, GPU memory fragmentation — and write the postmortems that prevent the next ten. Define SLOs, build the observability to measure them, and own the on-call rotation that defends them
• Partner Across the Stack: Work directly with researchers and ML engineers to take experimental workloads from "runs on one node" to "runs in production." You're a systems partner, not a ticket queue