
Member of Technical Staff - Infrastructure
Required skills
- AI workloads
- production systems
- scale
- distributed systems
- Linux
- networking
- storage
- concurrency
- performance engineering
- cloud infrastructure
Nice to have
- reinforcement learning infrastructure
- rollout generation
- distributed RL training
- model serving
- batching
- caching
Role summary
Warp Sciences is seeking an infrastructure and systems engineer to build the foundation for production AI workloads. This role involves working across core infrastructure, data systems, and the training and inference stack to ensure these systems are reliable, efficient, and scalable. The ideal candidate will have a strong track record of building and operating production systems at scale, deep systems expertise, practical experience with AI workloads, and the ability to balance performance, reliability, cost, and engineering speed. Responsibilities include designing and operating infrastructure, optimizing GPU utilization and data storage, ensuring production reliability, and improving deployment and orchestration tooling. Experience with AI tools and an AI-native approach is highly valued.
About Warp
At Warp Sciences, our mission is to help the world discover new medicines faster—and build the capacity to do it here in America. We bring AI, robotics, and life sciences together in automated labs that design, run, and learn from experiments, with each experiment making the next one better. We’re not developing drugs of our own. We’re building the infrastructure for the people who do.
We’re looking for an infrastructure and systems engineer to build the foundation for production AI workloads. You’ll work across core infrastructure, data systems, and the training and inference stack, making these systems reliable, efficient, and easy to operate as they scale. The inference stack is a combination of local robotics models and perception models and cloud foundation models and the surrounding runtime.
Our strongest requirement is a track record of building and operating production systems that have scaled. You should bring deep systems expertise, practical experience with AI workloads, and the judgment to balance performance, reliability, cost, and engineering speed. You should be ai pilled and using swarm of agents for development.
What you’ll do
Design, build, and operate infrastructure for large-scale AI training and inference.
Design & 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 infernece.
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 we’re looking for
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.
The ability to profile systems, identify bottlenecks, and deliver measurable improvements in performance, reliability, or cost.
An AI-native approach: comfortable working closely with model teams, understanding how AI workloads behave, and using AI tools effectively in your own engineering workflow.
Strong ownership, clear communication, and sound judgment when requirements are ambiguous.
Helpful, but not required
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
What success looks likeYou build infrastructure that holds up under real production demand. Training runs use compute efficiently, inference meets latency and throughput targets, data pipelines are dependable, and failures are easy to detect and diagnose. Your work gives the AI team a reliable foundation for shipping and scaling.
Equal Employment OpportunityWarp is an equal opportunity employer. We consider qualified applicants without regard to race, color, religion, national origin, ancestry, sex (including pregnancy, childbirth, breastfeeding, and related medical conditions), gender, gender identity or expression, sexual orientation, age, physical or mental disability, medical condition, genetic information, marital status, military or veteran status, reproductive health decision-making, or any other characteristic protected by applicable law.Reasonable AccommodationsWarp provides reasonable accommodations to qualified applicants with disabilities in accordance with applicable law. If you need an accommodation during the application or interview process, please contact recruiting@warp.xyzFair Chance HiringWarp considers qualified applicants with arrest or conviction records in accordance with the California Fair Chance Act and other applicable laws.General NoticeThis job posting describes the position generally and does not constitute an offer of employment or an employment contract.
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