Distributed Systems Engineer
2 days ago
As a Distributed Systems Engineer at Restored Cloud, you will be key in designing and optimizing distributed infrastructure tailored for large-scale AI/ML model training and inference. Your primary focus will address challenges like minimizing checkpointing delays, enabling seamless fault recovery, and maximizing resource utilization for models exceeding 1B parameters.
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 PyTorch Distributed, TensorFlow Distributed, or DeepSpeed.
- 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 1B+ 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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India Restored Cloud Full timeAs a Distributed Systems Engineer at Restored Cloud, you will be key in designing and optimizing distributed infrastructure tailored for large-scale AI/ML model training and inference. Your primary focus will address challenges like minimizing checkpointing delays, enabling seamless fault recovery, and maximizing resource utilization for models exceeding 1B...
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