ML Research Scientist

As a ML Research Scientist at Cerebras Systems, you will be tasked with developing novel training algorithms and network architectures, conducting research experiments, and publishing your findings at leading machine learning conferences. Your work will focus on enhancing model quality and compute efficiency by leveraging our unique hardware.

To qualify for the ML Research Scientist position at Cerebras Systems, candidates should have a strong grasp of machine learning theory and experience with frameworks like PyTorch and Jax. A strong research background, including publications in top conferences or journals, is highly desirable, along with a PhD in a relevant discipline.

In addition to the core skills in machine learning, preferred qualifications for a ML Research Scientist role at Cerebras Systems include experience with state-of-the-art transformer models, distributed training frameworks, and training speed optimizations. Familiarity with low-precision numerics and advanced optimizers could also be beneficial.

Cerebras Systems offers a unique atmosphere that combines startup vitality with job stability. Our team members appreciate the opportunity to build groundbreaking AI technology, publish their research, and work within a culture that respects individual beliefs and encourages continuous learning.

The Core ML team at Cerebras Systems fosters professional growth through collaborative projects, mentorship from experienced researchers, and ample opportunities to engage with the wider academic community. Team members are encouraged to pursue innovative research that can lead to significant advancements in AI technology.

In your response, highlight specific projects where you've implemented ML solutions using PyTorch or Jax. Discuss your familiarity with their unique features and how you've leveraged them to optimize machine learning models.

Discuss your methodology for identifying areas for improvement in existing algorithms. Include examples of past successes and describe your process for experimenting and validating new ideas in a rigorous fashion.

Provide an overview of a specific ML challenge you faced, the approach you took to analyze it, the algorithms you employed, and the outcome of your efforts. It's essential to demonstrate your problem-solving skills and creativity.

Be prepared to mention specific papers and explain how they shaped your understanding of machine learning principles or inspired your research directions. Discuss how these influences translate to your practice.

Explain your approach to testing and validating your algorithms. Highlight your methods for benchmarking performance and assessing robustness, including techniques like cross-validation and thorough experimentation.

In your response, detail any project experience using distributed training frameworks and describe how you optimized your training processes for performance and efficiency in a team environment.

Your answer should cover specific challenges related to sparse training, including issues with model performance, and how you've developed strategies or algorithms to address these issues based on your past experiences.

Discuss your approach to continuous learning, including following relevant journals, participating in conferences, or being involved in research communities. Emphasize your commitment to advancing your knowledge in ML.

Recap your experience presenting research findings, including the conference details, audience engagement, and feedback received. Highlight the impact your presentation had on your professional development.

Discuss your specific skills and experiences that align with Cerebras Systems' mission. Emphasize how your past work and innovative ideas can uniquely contribute to the development goals of the Core ML team.