Distributed systems engineer
3 weeks ago
Key Responsibilities:
Develop and scale distributed systems tailored for high-performance AI/ML workloads, focusing on eliminating delays caused by traditional checkpointing.
Design fault-tolerant and high-availability systems that ensure seamless operation and rapid recovery, even during infrastructure failures.
Implement advanced data partitioning, synchronization, and parallel computation techniques to handle terabytes of data and optimize memory usage across multi-node setups.
Collaborate with ML and infrastructure engineers to design innovative solutions for distributed training and inference of large-scale models.
Identify and resolve performance bottlenecks, particularly those arising from storage, memory, or network constraints in AI workflows.
Stay at the forefront of emerging distributed computing trends, such as zero-copy memory sharing, efficient in-memory data storage, and distributed model execution, to ensure your solutions remain cutting-edge.
Ability to adapt to new technologies and take on new responsibilities and roles in a fast-paced growing company.
Minimum Qualifications:
Bachelor's degree in Computer Science, Distributed Systems, Computer Engineering, or a related field.
5+ years of experience in designing and implementing distributed systems.
Proficiency in programming languages such as Python, C++, or Java.
Strong understanding of distributed computing principles, including fault tolerance, synchronization, and parallel computation.
Experience with distributed training frameworks such as Py Torch Distributed, Tensor Flow Distributed, or Deep Speed.
Familiarity with cloud platforms (AWS, GCP, or Azure) and managing multi-node infrastructure.
Demonstrated ability to troubleshoot performance bottlenecks in distributed systems.
Preferred Qualifications:
Master’s or Ph. D. in Computer Science, Distributed Systems, Computer Engineering, or a related field.
7+ years of hands-on experience with large-scale distributed systems for AI/ML workloads.
Expertise in advanced distributed systems concepts, such as zero-copy memory sharing, RDMA, and NVMe-based storage.
Experience working at Nvidia, AMD, AWS, or a similar distributed systems-focused organization.
Proven track record of optimizing distributed systems for AI/ML models with 1 B+ parameters.
Strong knowledge of network optimization techniques for high-performance computing.
Familiarity with cutting-edge AI/ML trends and the ability to integrate them into distributed architectures.
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