Senior ML Systems Engineer

About The Role You will engineer the critical path for AI workloads — feature pipelines, vector retrieval, and low-latency model serving — across our enterprise platform deployments. About The Role You will engineer the critical path for AI workloads — feature pipelines, vector retrieval, and low-latency model serving — across our enterprise platform deployments. Responsibilities Design feature stores with offline + online parity (sub-50 ms p99 reads) Build retrieval augmented systems on Iceberg / Delta with embedded vector indexes Operate model serving infrastructure — Triton, vLLM, or custom runtimes Define SLOs for inference latency, freshness, and quality Partner with applied ML teams to harden their notebooks into platforms Requirements 5+ years engineering production ML systems Strong Python + at least one systems language (Rust or Go) Deep familiarity with at least one of: feature stores, vector DBs, model servers Comfort with distributed systems, observability, and on-call Benefits Fully remote within US Top-of-market base + equity Conference budget and OSS contribution time Premium health benefits and 401(k) match #J-18808-Ljbffr