Senior ML Integration and Ops Engineer

As a Senior ML Integration and Ops Engineer at Cerebras Systems, your primary responsibilities include overseeing the integration of software and hardware components to facilitate large-scale machine learning model training. You will drive technical projects across various teams, enhance integration methodologies, automate workflows, and implement testing practices. A strong emphasis on effective communication and debugging skills is essential to ensure a smooth operation of our innovative technologies.

To qualify for the Senior ML Integration and Ops Engineer role at Cerebras Systems, you should possess a Master’s degree in computer science or electrical engineering alongside at least six years of relevant industry experience. Your expertise should include product validation for compute and machine learning systems, strong programming skills in languages such as Python, C++, or Go, and familiarity with frameworks like TensorFlow or PyTorch. Hands-on experience with large-scale distributed systems and automation practices will also be beneficial.

Preferred skills for the Senior ML Integration and Ops Engineer position at Cerebras Systems include hands-on experience training large language models (LLMs) and working with containers and Kubernetes. Experience in managing complex projects across multiple teams and driving collaboration in a fast-paced environment is valued. Furthermore, a deep understanding of ML workflows enhances your contribution to our innovative practices.

Cerebras Systems emphasizes a simple, non-corporate work culture that respects individual beliefs and promotes diversity and inclusion. As a Senior ML Integration and Ops Engineer, you will thrive in an environment that values continuous learning and supports personal growth. The company actively celebrates different backgrounds, perspectives, and skills among its employees, believing that diverse teams create better products.

New hires joining Cerebras Systems as a Senior ML Integration and Ops Engineer can expect to work on cutting-edge technologies in AI and contribute directly to transformative projects. They will be part of a dynamic team that is passionate about innovation and collaboration. Additionally, employees can enjoy job stability alongside the excitement and opportunities of a startup environment, empowering them to excel in their roles.

For this question, detail specific projects where you led or contributed to large-scale ML model training. Highlight the methodologies you employed, tools you used, and the outcomes of those projects. This showcases both your technical expertise and project management skills.

Discuss a structured approach to debugging in distributed environments, such as using logging frameworks, monitoring tools, or specific debugging techniques tailored for complex systems. Providing examples of successful resolutions reinforces the effectiveness of your approach.

Explain your communication strategies and tools you use for effective collaboration. Highlight experiences where you bridged differing viewpoints or resolved conflicts, demonstrating your ability to work well within multidisciplinary teams.

Share specifics about automation tools or frameworks you've used, like Jenkins, Ansible, or custom scripts for deployment and testing. Describe how you employed them to optimize workflows and improve operational efficiency.

Provide a clear example of a project where you successfully applied integration methodologies. Discuss the challenges faced, how you approached them, and the impact of your methods on the project's success. This illustrates your problem-solving aptitude.

Discuss your systematic approach to testing and validation in machine learning, including unit tests, integration tests, and how you ensure that the models meet performance benchmarks. This showcases a thorough understanding of quality assurance in ML.

Describe your organizational methods, whether through prioritization frameworks like MoSCoW or using project management tools. Provide a scenario demonstrating your effectiveness in handling multiple responsibilities without compromising quality.

Identify the specific languages you are proficient in, such as Python or C++, and detail how you've utilized them for automation tasks. Illustrate with examples of scripts or tools you've developed to streamline processes in your previous roles.

Discuss design principles such as modularity, scalability, and maintainability that are particularly relevant to ML applications. Provide insights into how these principles inform your development practices and contribute to long-term project success.

Convey your enthusiasm for Cerebras Systems' revolutionary technology and its impact on the AI industry. Share specific aspects of the company culture, projects, or innovations that resonate with you and align with your career aspirations.