Member of Technical Staff - Infrastructure

Warp • South San Francisco, CA

4d

About The Position

Warp Sciences is building scientific infrastructure in the United States to accelerate the discovery of new medicines by bringing AI, robotics, and life sciences together in increasingly automated laboratories. The company builds infrastructure for research teams rather than maintaining a proprietary drug pipeline.

The Member of Technical Staff will build the infrastructure foundation for production AI workloads across core infrastructure, data systems, and the training and inference stack. The inference stack includes local robotics and perception models, cloud foundation models, and their runtime. The role requires practical AI workload experience, deep systems expertise, and judgment to balance performance, reliability, cost, and engineering speed. The engineer is expected to build and operate scalable production systems and to use AI tools effectively in their development workflow.

Success means delivering infrastructure that remains reliable under production demand: efficient training compute, inference that meets latency and throughput targets, dependable data pipelines, and failures that can be readily detected and diagnosed.

Requirements

• A demonstrated track record of designing, shipping, and operating production systems at scale, with ownership beyond the initial launch.
• Strong fundamentals in distributed systems, Linux, networking, storage, concurrency, and performance engineering.
• Experience building and operating cloud infrastructure, container orchestration, and infrastructure as code.
• Experience with data infrastructure, including distributed processing, pipelines, and storage systems.
• Hands-on experience supporting and optimizing AI training and inference workloads, including GPU infrastructure and distributed execution.
• Ability to profile systems, identify bottlenecks, and deliver measurable improvements in performance, reliability, or cost.
• An AI-native approach, including comfort working closely with model teams, understanding AI workload behavior, and using AI tools effectively in engineering workflows.
• Strong ownership, clear communication, and sound judgment when requirements are ambiguous.

Nice To Haves

• Experience with reinforcement learning infrastructure, rollout generation, or distributed RL training.
• Experience with model serving, batching, caching, quantization, or other inference optimization techniques.
• Experience with GPU scheduling, high-performance networking, or heterogeneous compute environments.
• Experience with robotics.

Responsibilities

• Design, build, and operate infrastructure for large-scale AI training and inference.
• Design and build reliable data infrastructure for ingestion, processing, storage, and delivery to training and evaluation workflows.
• Optimize GPU utilization, distributed training performance, inference latency and throughput, and infrastructure cost.
• Optimize data storage and access for training and inference.
• Own production reliability through observability, capacity planning, incident response, and recovery.
• Improve deployment, orchestration, and developer tooling so engineers and researchers can move quickly.
• Partner with AI engineers and researchers to turn experimental workloads into dependable production systems.
• Diagnose bottlenecks across compute, networking, storage, data pipelines, and model execution.

What This Job Offers

• Job Type: Full-time
• Education Level: No Education Listed