Distributed Systems Engineer, Data & Inference Platform

adaption

Distributed Systems Engineer building efficient intelligence systems for real-time adaptation and collaboration. Designing distributed inference systems and large-scale data pipelines while optimizing performance and reliability.

Posted 5/7/2026full-timeSan Francisco • California • 🇺🇸 United StatesMid-LevelSeniorWebsite

Tech Stack

Tools & technologies

Distributed SystemsGoKubernetesNode.jsPythonRayRustSpark

About the role

Key responsibilities & impact

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.

Requirements

What you’ll need

5+ years building and operating distributed systems in production.
Deep experience with at least one large-scale data or compute framework (Ray, Spark, Flink, Beam, Dask).
Strong fluency in Python and at least one systems language (Go, Rust, C++).
Working knowledge of the GPU/accelerator stack: CUDA fundamentals, NCCL, mixed precision, memory layout. You don't need to write kernels, but you should know why a workload is bound by what it's bound by.
Experience operating Kubernetes-based infrastructure, including custom operators or schedulers.
A track record of owning hard production incidents end-to-end — diagnosis, mitigation, and the durable fix.
Bonus: hands-on experience with LLM inference engines (vLLM, SGLang, TensorRT-LLM, TGI), modern lakehouse formats (Iceberg, Delta, Hudi), or open-source contributions to relevant projects.

Benefits

Comp & perks

Flexible work: In-person collaboration in the Bay Area, a distributed global-first team, and team offsites.
Adaption Passport: Annual travel stipend to explore a country you've never visited. We're building intelligence that evolves alongside you, so we encourage you to keep expanding your horizons.
Lunch Stipend: Weekly meal allowance for take-out or grocery delivery.
Well-Being: Comprehensive medical benefits and generous paid time off.

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

distributed systemslarge-scale data frameworksRaySparkFlinkBeamDaskPythonGoRust

Soft Skills

problem-solvingcollaborationcommunicationdiagnosismitigationincident management