Member of Technical Staff, Performance & Capacity

Superintelligence

Performance and capacity engineer optimizing GPU fleets, networking, storage, and workloads. Supporting Physical Superintelligence’s AI systems for scalable scientific discovery and new physics.

Posted 8/16/2026full-timeBoston • Massachusetts • 🇺🇸 United StatesLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in optimizing GPU performance for large-scale compute workloads, with a strong focus on capacity modeling, parallel file systems, and interconnect behavior. Capable of clearly communicating complex performance results to researchers and making informed capacity decisions with financial implications.

Highest-signal resume keywords

GPU Performance OptimizationDistributed Training PerformanceCapacity Decision OwnershipParallel File Systems KnowledgeInfiniBand Networking Expertise

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

GPU WorkloadsLarge-Scale ComputePerformance Bottleneck AnalysisAI Training and InferenceCommunication/Compute OverlapBatchingLatency FloorsError Bar AnalysisCheckpointing EconomicsData Locality Optimization

Soft Skills

Clear CommunicationProblem-Solving

Tools & Technologies

InfiniBandRDMA NetworkingParallel File Systems

Industry Keywords

Capacity ModelingMulti-Node GPU TrainingTraining ThroughputHPC BackgroundData Center Experience

Tech Stack

Tools & technologies

CloudNode.js

About the role

Key responsibilities & impact

Measure fleet utilization, goodput, and training efficiency on real workloads
Identify and close sources of wasted capacity
Own the capacity model, including node counts, assumptions, and error bars
Defend capacity decisions when real money is committed
Own workload mix, preemptible fraction, and checkpointing economics over time
Optimize parallel file systems, data locality, and interconnect behavior for multi-node GPU training
Locate actual performance bottlenecks beyond profiler summaries
Explain performance results to researchers in usable terms

Requirements

What you’ll need

Five or more years with GPU and large-scale compute workloads
Real multi-node experience with distributed training performance, interconnects, and collective communication
System-level knowledge of GPU performance characteristics, including H100 and B200 workloads
Deep understanding of AI training and inference performance
Understanding of step time, MFU, communication/compute overlap, decode bandwidth, batching, and latency floors
Experience owning a capacity decision with financial consequences
Fluent knowledge of parallel file systems and their impact on training throughput
Fluent knowledge of InfiniBand or RDMA-class networking behavior
Ability to explain performance results clearly to researchers
Work authorization/sponsorship may be relevant for the United States
Nice to have: data center floor experience, thermal and power envelopes, failure domains, energy- or preemption-aware scheduling, cloud and specialist GPU provider sourcing, and scientific computing or HPC background

Benefits

Comp & perks

Meaningful early-stage equity
Competitive compensation
Benefits
Full ownership of work from spec to ship to on-call
AI-native development process with daily use of agentic coding tools