Description
We are looking for a technical expert to join our team and enhance the robustness and scalability of the infrastructure to support scaling our Machine Learning workloads. This role will involve working across various areas, from enhancing underlying HPC infrastructure to optimizing Kubernetes and Kubeflow setups, as well as refining training infrastructure components. You will collaborate closely with datacenter engineers, ML engineers, and vendors to diagnose and resolve ML infrastructure issues effectively.
- Enhance the resilience and reliability of our Machine Learning platform infrastructure.
- Optimize Kubernetes clusters and manage Kubeflow deployments.
- * Streamline and optimize training infrastructure to improve efficiency and scalability.
- Collaborate closely with cross-functional teams to understand requirements and implement solutions.
- Troubleshoot complex HPC infrastructure issues and implement effective resolutions with partner team.
- Implement best practices for monitoring, logging, and alerting to ensure system health.
Additional Description
- Bachelor's degree in Computer Science, Engineering, or related field; Master's degree preferred.
- Proven experience (8+ years) in designing and building scalable data platforms and data pipelines.
- Experience optimizing and scaling distributed systems for Machine Learning workloads or equivalent workloads.
- Strong proficiency in managing and optimizing Kubernetes clusters.
- Knowledge of cloud platforms (e.g., AWS, GCP, Azure) and their Machine Learning services.
- In-depth understanding of network stacks, including protocols such as TCP/IP, HTTP, DNS, and others.
- Strong problem-solving skills and ability to work both independently and collaboratively.
- Excellent communication skills and ability to interact effectively with stakeholders.
This role is categorized as hybrid. This means the successful candidate is expected to report to Mountain View three times per week, at minimum.
The compensation information is a good faith estimate only. It is based on what a successful applicant might be paid in accordance with applicable state laws. The compensation may not be representative for positions located outside of New York, Colorado, California, or Washington.
- The salary range for this role is $225,000 and $334,800. The actual base salary a successful candidate will be offered within this range will vary based on factors relevant to the position.
An incentive pay program offers payouts based on company performance, job level, and individual performance.
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Diversity Information
General Motors is committed to being a workplace that is not only free of unlawful discrimination, but one that genuinely fosters inclusion and belonging. We strongly believe that workforce diversity creates an environment in which our employees can thrive and develop better products for our customers. We encourage interested candidates to review the key responsibilities and qualifications for each role and apply for any positions that match their skills and capabilities. Applicants in the recruitment process may be required, where applicable, to successfully complete a role-related assessment(s) and/or a pre-employment screening prior to beginning employment. To learn more, visit How we Hire
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General Motors is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.
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At General Motors, our vision is to create a world with Zero Crashes, Zero Emissions, and Zero Congestion. We wholeheartedly embrace the responsibility to lead the change that will make our world better, safer, and more equitable for all.
Our industry and company are undergoing a once-in-a-lifetime technological transformation, which is reshaping our approach to technology and innovation. We are expanding our horizons through new technology platforms and driving innovations that deliver exceptional value to our customers.
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Hybrid Workspace
Employees engage in a combination of remote and on-site work.
Roles that are categorized as Hybrid mean that the successful candidate is expected to report onsite to the designated facility at least three times per week or other frequency as dictated by the business.
