ML Systems Performance Engineer

Cerebras

. Build kernel-level and end-to-end performance models to estimate performance of state-of-the-art and customer ML models .

Posted 11/6/2026full-timeBengaluru • IndiaMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in kernel optimization and performance modeling for machine learning applications, with a strong foundation in computer architecture and proficiency in C++ and Python. Capable of developing tools for performance visualization and debugging in high-performance computing environments.

Highest-signal resume keywords

Kernel OptimizationPerformance ModelingC++ ProgrammingPython ProgrammingComputer Architecture

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Kernel Microcode DebuggingCompiler AlgorithmsPerformance ProfilingDeep Learning MathHPC ExperienceCPU/GPU PerformanceCPU/GPU SimulatorsAnalytical Problem-Solving

Soft Skills

Analytical MindsetProblem-Solving

Tools & Technologies

Wafer Scale EngineCompute Cluster

Industry Keywords

Machine LearningEnd-to-End Model InferenceRuntime PerformancePerformance Data Visualization

Tech Stack

Tools & technologies

PythonC++

About the role

Key responsibilities & impact

Build kernel-level and end-to-end performance models to estimate performance of state-of-the-art and customer ML models
Optimize and debug kernel microcode and compiler algorithms to improve ML model inference speed, throughput and compute utilization on the Cerebras WSE
Debug and understand runtime performance on the system and cluster
Develop tools and infrastructure to visualize performance data collected from the Wafer Scale Engine and compute cluster
Operate at the intersection of hardware and software to drive end-to-end model inference speed and throughput

Requirements

What you’ll need

Bachelors / Masters / PhD in Electrical Engineering or Computer Science
Strong background in computer architecture
Exposure to and understanding of low-level deep learning / LLM math
Strong analytical and problem-solving mindset
3+ years of experience in a relevant domain (Computer Architecture, CPU/GPU Performance, Kernel Optimization, HPC)
Experience working on CPU/GPU simulators
Exposure to performance profiling and debug on any system pipeline
Comfort with C++ and Python

Benefits

Comp & perks

Build a breakthrough AI platform beyond the constraints of the GPU
Publish and open source cutting-edge AI research
Work on one of the fastest AI supercomputers in the world
Job stability with startup vitality
Simple, non-corporate work culture that respects individual beliefs
Continuous learning, growth and support