Distributed Systems Engineer, Data & Inference Platform

Adaption Labs; San Francisco (CA); Hybrid; USD 120,000 - 180,000; Full time

Benefits offered by this job

Flexible work

Annual travel stipend

Weekly meal allowance

Comprehensive medical benefits

Job summary

Adaption Labs is seeking a Distributed Systems Engineer to build and operate systems that create intelligence from raw compute. This role involves optimizing distributed inference systems for LLMs and constructing large-scale data pipelines. The ideal candidate has over 5 years of experience with distributed systems, strong Python skills, and knowledge of data frameworks like Ray and Spark. Benefits include flexible work arrangements, travel stipends, and comprehensive medical coverage.

Qualifications

• 5+ years building and operating distributed systems in production.
• Deep experience with at least one large-scale data or compute framework.
• Strong fluency in Python and at least one systems language.

Responsibilities

• Design and operate distributed inference systems for LLMs.
• Build large-scale data pipelines that ingest and transform datasets.
• Debug failure modes under real production traffic.

Skills

Distributed systems in production

Large-scale data frameworks (Ray, Spark, Flink)

Fluency in Python

Systems languages (Go, Rust, C++)

Kubernetes infrastructure

Job description

Distributed Systems Engineer, Data & Inference Platform

The Role

You’ll build and operate the systems that turn raw compute into useful intelligence — the inference services that serve LLMs at scale and the data pipelines that feed them. One week you’re hunting a tail-latency regression in a production inference service handling millions of requests; the next you’re redesigning a Ray Data pipeline so it stops melting down at petabyte scale. The work spans architecture, implementation, and the on‑call pager that keeps you honest about both. Researchers and ML engineers will hand you workloads that barely run; you’ll hand them back systems that run reliably, efficiently, and cheaply enough to matter.

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.

Qualifications

• 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.
• Above all, we’re looking for great teammates who make work feel lighter and aren’t afraid to go out on a limb with bold ideas. You don’t need to be perfect, but you do need to be adaptable. We encourage you to apply, even if you don’t check every box.

Benefits

• 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.