Build and validate simulators for AI cluster performance, energy consumption, scheduling, and resource optimization. Benchmark workloads on simulated and real GPU or accelerator hardware, compare results against measurements, and support technical product research. The role requires advanced computer science or electrical and computer engineering knowledge, coding expertise, GPU and accelerator architecture understanding, LLM training and inference knowledge, and familiarity with simulation, benchmarking, power measurement, and cluster resource allocation.
Research Engineer, AI Cluster Performance
About XPerf
XPerf builds software that validates, operates, and optimizes artificial intelligence (AI) clusters built on graphics processing units (GPUs) and other accelerators. Our products are ClusterReady for pre-deployment cluster validation, ClusterBeacon for runtime observability, and ClusterMarshal for scheduling and prediction. We were founded by ex-Intel engineers who have deployed clusters with thousands of accelerators.
The role
This is a research role. You will build a simulator that lets us benchmark the performance and energy consumption of jobs and workloads, and optimize how they use resources. ASTRA-sim is at the core of this work, and we also use higher-level simulators that model whole clusters and data centers, including job scheduling, power, and cooling.
What you'll do
- Build a simulator that models how jobs and workloads run on GPUs and other accelerators
- Model energy consumption alongside performance
- Benchmark workloads in the simulator and on real hardware, and use the measurements to keep the simulator accurate
- Use the results to optimize resource use and energy consumption
- Support technical research on our products when needed
Work environment
We are a small technical team, and we move fast. Our customers are overseas, so working hours are flexible and some work falls outside the usual US workday.
Basic requirements
- Master's degree or higher in Electrical and Computer Engineering or Computer Science
- Solid coding skills
- Basic knowledge of GPU architecture and other AI accelerators
- Understanding of the training and inference process on GPUs, and how each consumes energy
- Understanding of large language model (LLM) algorithms
- Understanding of how simulators work
- Ability to master AI tools quickly and use them in your daily work
- Ability to turn open research questions into clear requirements
- Comfort with a fast pace and flexible hours
Preferred qualifications
Education
- Doctor of Philosophy (PhD) in Electrical and Computer Engineering or Computer Science
Simulators
- Experience building or extending a simulator
- Experience with or knowledge of ASTRA-sim, our core tool for distributed training performance
- Experience with or knowledge of any related simulator, such as:
- SustainCluster for energy- and carbon-aware AI scheduling across data centers
- ExaDigiT for data center digital twins covering job scheduling, power, and cooling
- Blox for cluster scheduling
- SimAI for full-stack LLM training and inference
- Vidur for LLM inference
- LLMServingSim for LLM serving performance and power
- Calculon for analytical transformer performance modeling
- ATLAHS for network simulation from application traces
- ttsim for Tenstorrent chips
- Experience checking simulated results against real measurements
Benchmark testing
- MLPerf Training and MLPerf Inference, including LoadGen
- GPU communication tests: nccl-tests and rccl-tests
- Network and storage tests: ib_write_bw, iperf3, and fio
- LLM serving benchmarks with vLLM or SGLang, measuring tokens per second, time to first token (TTFT), and latency
- GPU monitoring with NVIDIA Data Center GPU Manager (DCGM)
Energy
- Measuring power and energy with tools such as nvidia-smi, amd-smi, or Running Average Power Limit (RAPL)
- Modeling energy consumption in a simulator
Accelerators and resource optimization
- Hands-on experience with accelerators other than GPUs
- Job scheduling and resource allocation on clusters that mix accelerator types
- Measuring utilization and useful work completed
Coding
- 5+ years of coding experience
Equity & benefits: Meaningful stock options. Solid health/dental/vision, flexible time off, professional development support.
Direct candidates only, no recruiters.
Similar Jobs
Information Technology
Design and develop production-grade optimization models and services for workforce planning, forecasting, scheduling, and resource allocation. Build OR-Tools/CP-SAT constraint models, validate schedule quality and solver performance, and integrate optimization capabilities into an enterprise workforce management platform. Collaborate with product, architecture, engineering, and workforce-planning stakeholders to translate business requirements into mathematical models and operationally useful schedules.
Top Skills:
AWSAzureC++ClaudeCp-SatGCPGithub CopilotJavaOr-ToolsPython
Aerospace • Security • Cybersecurity • Industrial
Researches and develops AI, machine learning, computer vision, robotics, signal processing, and control algorithms for autonomous defense and commercial systems. Builds and tests prototypes, trains and optimizes models, defines algorithm requirements, and collaborates across perception, planning, simulation, and testing teams. The role also includes developing MLOps demonstrations, proof-of-concept environments, technical presentations, customer discovery support, RFP responses, and sales engineering enablement.
Top Skills:
ArgoComputer VisionControl SystemsDeep LearningDockerGenerative AiKserveKubeflowKubernetesLarge Language ModelsMachine LearningMlflowMlopsPanelPythonPyTorchRayRetrieval-Augmented GenerationRoboticsScikit-LearnSignal ProcessingTensorFlowWeights & Biases
Aerospace • Security • Cybersecurity • Industrial
Develop and deploy scalable Generative AI and machine learning solutions for engineering workflows. Responsibilities include identifying AI opportunities, building prototypes and production applications, designing LLM retrieval and agentic workflows, integrating APIs and data, implementing testing and monitoring, and applying cybersecurity, DevSecOps, architecture, and responsible AI governance. The role partners with engineering, architecture, data, and IT teams to mature secure enterprise AI capabilities.
Top Skills:
Ai/MlAPIsAutogenAWSC++CrewaiDevsecopsGenerative AiGraphragKnowledge GraphsLanggraphLarge Language ModelsLlamaindexModel Context ProtocolPrompt EngineeringPythonRagVector Search
What you need to know about the Austin Tech Scene
Austin has a diverse and thriving tech ecosystem thanks to home-grown companies like Dell and major campuses for IBM, AMD and Apple. The state’s flagship university, the University of Texas at Austin, is known for its engineering school, and the city is known for its annual South by Southwest tech and media conference. Austin’s tech scene spans many verticals, but it’s particularly known for hardware, including semiconductors, as well as AI, biotechnology and cloud computing. And its food and music scene, low taxes and favorable climate has made the city a destination for tech workers from across the country.
Key Facts About Austin Tech
- Number of Tech Workers: 180,500; 13.7% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Dell, IBM, AMD, Apple, Alphabet
- Key Industries: Artificial intelligence, hardware, cloud computing, software, healthtech
- Funding Landscape: $4.5 billion in VC funding in 2024 (Pitchbook)
- Notable Investors: Live Oak Ventures, Austin Ventures, Hinge Capital, Gigafund, KdT Ventures, Next Coast Ventures, Silverton Partners
- Research Centers and Universities: University of Texas, Southwestern University, Texas State University, Center for Complex Quantum Systems, Oden Institute for Computational Engineering and Sciences, Texas Advanced Computing Center
