Note: The job is a remote job and is open to candidates in USA. Cerebras Systems builds the world's largest AI chip, transforming the user experience of AI applications with industry-leading training and inference speeds. The Senior Runtime Engineer will design and develop high-performance distributed software for massive compute and data pipelines, pushing the limits of concurrency, throughput, and scalability.
Responsibilities
- Design and implement distributed runtime components to efficiently manage large-scale execution workloads
- Develop and optimize high-performance data and communication pipelines that fully utilize CPU, memory, storage, and network resources
- Enable scalable execution across multiple compute nodes, ensuring high concurrency and minimal bottlenecks
- Collaborate closely with ML and compiler teams to integrate new model architectures, training regimes, and hardware-specific optimizations
- Diagnose and resolve complex performance issues across the software stack using profiling and instrumentation tools
- Contribute to overall system design, architecture reviews, and roadmap planning for large-scale AI workloads
Skills
- 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
- 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
Benefits
- Job stability with startup vitality
- Simple, non-corporate work culture that respects individual beliefs
- Equal and diverse environment
- Inclusive teams build better products and companies
- Work environment that empowers people to do their best work through continuous learning, growth and support of those around them
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