Senior Runtime Performance Engineer

Cerebras Systems; Toronto; On-site; CAD 80,000 - 150,000; Full time

Job summary

Join a pioneering company at the forefront of AI technology as a Performance Engineer. This role offers the opportunity to work on groundbreaking AI architectures and optimize high-performance ML training and inference solutions. You will engage with the AI and ML developer community, collaborating with diverse teams to elevate the computational platform. With a culture that values innovation and individual beliefs, this position promises job stability and the chance to contribute to cutting-edge advancements in AI. If you are passionate about performance engineering and eager to make an impact, this is the perfect opportunity for you.

Benefits

Job stability with startup vitality

Non-corporate work culture

Opportunity to publish AI research

Work on one of the fastest AI supercomputers

Continuous learning and growth support

Qualifications

• 5+ years in performance engineering with a focus on algorithm optimization.
• Strong skills in C/C++ and familiarity with Python or scripting languages.

Responsibilities

• Optimize CPU and memory subsystems for ML workloads on x86 machines.
• Develop algorithms for data movement and synchronization in software.

Skills

C/C++

Python

Performance Engineering

Memory Subsystem Optimization

Distributed Systems

Algorithm Optimization

Compiler Technologies (LLVM, MLIR)

Machine Learning Frameworks (PyTorch)

Education

BS in Computer Science

MS in Computer Engineering

PhD in a related field

Tools

AMD uprof

Job description

Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. Our novel wafer-scale architecture provides the AI compute power of dozens of GPUs on a single chip, with the programming simplicity of a single device. This approach allows Cerebras to deliver industry-leading training and inference speeds and empowers machine learning users to effortlessly run large-scale ML applications, without the hassle of managing hundreds of GPUs or TPUs.

Cerebras' current customers include global corporations across multiple industries, national labs, and top-tier healthcare systems. In January, we announced a multi-year, multi-million-dollar partnership with Mayo Clinic, underscoring our commitment to transforming AI applications across various fields. In August, we launched Cerebras Inference, the fastest Generative AI inference solution in the world, over 10 times faster than GPU-based hyperscale cloud inference services.

About the Role

Join Cerebras as a Performance Engineer within our innovative Runtime Team. Our groundbreaking CS-3 system, hosted by a distributed set of modern and powerful x86 machines, has set new benchmarks in high-performance ML training and inference solutions. It leverages a dinner-plate sized chip with 44GB of on-chip memory to surpass traditional hardware capabilities. This role will challenge and expand your expertise in optimizing AI applications and managing computational workloads primarily on the x86 architecture that run our Runtime driver.

Responsibilities

• Focus on CPU and memory subsystem optimizations for our Runtime software driver, enabling faster key cloud and ML training/inference workloads across modern x86 machines that form the backbone of our AI accelerator.
• Develop and enhance algorithms for efficient data movement, local data processing, job submission, and synchronization between various software and hardware components.
• Optimize our workloads using advanced CPU features like AVX instructions, prefetch mechanisms, and cache optimization techniques.
• Perform performance profiling and characterization using tools such as AMD uprof, and reduce OS level overheads.
• Influence the design of Cerebras' next-generation AI architectures and software stack by analyzing the integration of advanced CPU features and their impact on system performance and computational efficiency.
• Engage directly with the AI and ML developer community to understand their needs and solve contemporary challenges with innovative solutions.
• Collaborate with multiple teams within Cerebras, including architecture, research, and product management, to elevate our computational platform and influence future designs.

Skills And Qualifications

• BS, MS, or PhD in Computer Science, Computer Engineering, or a related field.
• 5+ years of relevant experience in performance engineering, particularly in optimizing algorithms and software design.
• Strong proficiency in C/C++ and familiarity with Python or other scripting languages.
• Demonstrated experience with memory subsystem optimizations and system-level performance tuning.
• Experience with distributed systems is highly desirable, as it is crucial to optimizing the performance of our Runtime software across multiple x86 hosts.
• Familiarity with compiler technologies (e.g., LLVM, MLIR) and with PyTorch and other ML frameworks.

Why Join Cerebras

People who are serious about software make their own hardware. At Cerebras we have built a breakthrough architecture that is unlocking new opportunities for the AI industry. With dozens of model releases and rapid growth, we’ve reached an inflection point in our business. Members of our team tell us there are five main reasons they joined Cerebras:

• Build a breakthrough AI platform beyond the constraints of the GPU.
• Publish and open source their cutting-edge AI research.
• Work on one of the fastest AI supercomputers in the world.
• Enjoy job stability with startup vitality.
• Our simple, non-corporate work culture that respects individual beliefs.

Read our blog: Five Reasons to Join Cerebras in 2025.

Apply today and become part of the forefront of groundbreaking advancements in AI!

Cerebras Systems is committed to creating an equal and diverse environment and is proud to be an equal opportunity employer. We celebrate different backgrounds, perspectives, and skills. We believe inclusive teams build better products and companies. We try every day to build a work environment that empowers people to do their best work through continuous learning, growth and support of those around them.