Senior Runtime Engineer

Cerebras Cerebras · Semiconductors · US and Canada Offices · Software

Senior Runtime Engineer at Cerebras, focusing on designing and developing high-performance distributed software for large-scale AI training and inference workloads on custom hardware. The role involves optimizing compute and data pipelines across heterogeneous clusters, pushing concurrency and scalability limits, and collaborating with ML and compiler teams.

What you'd actually do

  1. Design and implement distributed runtime components to efficiently manage large-scale execution workloads.
  2. Develop and optimize high-performance data and communication pipelines that fully utilize CPU, memory, storage, and network resources.
  3. Enable scalable execution across multiple compute nodes, ensuring high concurrency and minimal bottlenecks.
  4. Collaborate closely with ML and compiler teams to integrate new model architectures, training regimes, and hardware-specific optimizations.
  5. Diagnose and resolve complex performance issues across the software stack using profiling and instrumentation tools.

Skills

Required

  • 3+ years of experience developing high-performance or distributed system software.
  • Strong programming skills in C/C++, with expertise in multi-threading, memory management, and performance optimization.
  • Experience with distributed systems, networking, or inter-process communication.
  • Solid understanding of data structures, concurrency, and system-level resource management (CPU, I/O, and memory).
  • Proven ability to debug, profile, and optimize code across scales—from threads to clusters.
  • Bachelor’s, Master’s, or equivalent experience in Computer Science, Electrical Engineering, or related field.

Nice to have

  • Familiarity with machine learning training or inference pipelines, especially distributed training and large-model scaling.
  • Exposure to Python and PyTorch, particularly in the context of model training or performance tuning.
  • Experience with compiler internals, custom hardware interfaces, or low-level protocol design.
  • Prior work on high-performance clusters, HPC systems, or custom hardware/software co-design.
  • Deep curiosity about how to unlock new levels of performance for large-scale AI workloads.

What the JD emphasized

  • high-performance
  • distributed system software
  • multi-threading
  • memory management
  • performance optimization
  • distributed systems
  • concurrency
  • system-level resource management
  • debug, profile, and optimize code across scales

Other signals

  • large-scale AI systems
  • training and inference workloads
  • high-performance distributed software
  • heterogeneous clusters
  • concurrency, throughput, and scalability
  • systems engineering and machine learning performance
  • data ingestion to distributed execution
  • cutting-edge hardware platforms