# Research Platform Engineer

**Company:** [World Labs](https://hotfix.jobs/companies/world-labs)
**Location:** San Francisco, CA
**Role:** ML Engineering
**Experience:** 5+ years
**Skills:** Python, C++, CUDA, Rust, Go, Distributed Systems, ML Infrastructure, Gpu Utilization, Distributed Training, Inference Systems
**Posted:** 2026-05-01

> Builds mission-critical training, data, and inference infrastructure for AI research on large world models. Optimizes performance, debugs distributed systems, and improves researcher velocity with 5+ years experience in ML systems and strong Python proficiency.

## Job Description

## What You Will Do
- Design and build training infrastructure, data infrastructure, and data processing and sourcing pipelines.
- Productionize models for serving and own parts of the inference stack.
- Build internal tools and services that increase engineering and research velocity.
- Debug hard problems across training, inference, and performance — including distributed systems issues under real research workloads.
- Optimize throughput, latency, GPU utilization, and system-level scaling.
- Improve research iteration speed and developer experience — cut debugging time, raise reliability, and make it faster for researchers to ship experiments.
- Raise the engineering bar across research and platform code alike.

## Key Qualifications
- 5+ years of experience building and shipping production systems, with demonstrated ownership of infrastructure used by other engineers or researchers.
- Strong depth in at least one of: ML infrastructure, distributed training or inference systems, data systems, or research tooling.
- Strong distributed systems foundations — concurrency, consistency tradeoffs, replication, failure modes, and scaling behavior under real workloads.
- Strong performance optimization skills across at least one of: training throughput, inference latency, GPU utilization, or system-level scaling.
- Strong proficiency in Python, with the ability to work in C++, CUDA, Rust, or Go as the work demands.
- Experience working directly with ML researchers or research engineers, including productionizing research code.
- A product engineer's instincts for iteration speed and developer experience — applied to the systems researchers use every day.
- Strong judgment about what to build and what to leave alone, particularly when research requirements are ambiguous or shifting.
- High-ownership mindset; you measure yourself by outcomes shipped, not by tickets closed.

## Preferred Qualifications
- Experience at an AI lab or ML-native company, working on systems used directly by researchers.
- Experience with large-scale training or inference systems — GPU orchestration, distributed training, or high-throughput inference.
- Experience with low-level performance optimization — profiling, kernel-level tuning, memory and bandwidth optimization, distributed communication primitives.
- Experience building developer experience tooling for research — notebooks, experiment tracking, reproducibility infrastructure.
- Experience in early-stage or high-growth environments where scope and priorities shift frequently.

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