ML Research Scientist

Cerebras Systems • Sunnyvale, CA

293d

About The Position

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.

Requirements

• Strong grasp of machine learning theory, fundamentals, linear algebra, and statistics.
• Experience with machine learning frameworks, such as PyTorch and Jax.
• Strong track record of research success through relevant publications at top conferences or journals (e.g. ICLR, ICML, NeurIPS), or patents and patent applications.

Nice To Haves

• PhD in a relevant discipline.
• Experience with state-of-the-art transformer language models.
• Experience with distributed training concepts and frameworks, such as TorchTitan, Megatron/Deepspeed, or FairSeq FSDP.
• Experience with training speed optimizations, such as model architecture transformations to target hardware, or low-level kernel development (e.g., Triton).

Responsibilities

• Develop novel training algorithms that advance the state-of-the-art in model quality and compute efficiency.
• Develop novel network architectures that address foundational challenges in language and multi-modal domains.
• Co-design ML algorithms that take advantage of our unique hardware, and collaborate with engineers to co-design next-generation architectures.
• Design and run research experiments that show novel algorithms are efficient and robust.
• Analyze results to gain research insights, including training dynamics, gradient quality, and dataset preprocessing techniques.
• Publish and present research at leading machine learning conferences.

Benefits

• Competitive compensation
• Flexible working arrangements
• Ample computing resources
• Collaboration opportunities with leading academic groups
• Extensive options for professional growth and advancement in your AI journey

What This Job Offers

• Career Level: Entry Level
• Industry: Professional, Scientific, and Technical Services
• Education Level: Ph.D. or professional degree