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Machine Learning Infrastructure Architect
2 weeks ago
Job Overview
We are seeking a highly skilled Senior ML Infrastructure Engineer to join our team. The successful candidate will be responsible for designing, implementing, and optimizing large-scale machine learning infrastructure.
Key Responsibilities
- Design and implement scalable ML inference systems
- Promote hardware-software co-design to push the limits of performance
- Leverage libraries like PyTorch, DeepSpeed, and CCL/NCCL for distributed training and inference
- Develop C++ extensions for Python to fine-tune performance and enable seamless integration
- Build CI/CD pipelines, automate testing infrastructures, and ensure smooth delivery of enterprise-grade ML systems
Requirements
The ideal candidate will have 5+ years of hands-on software engineering experience, with a focus on building production-grade systems.
Additionally, the candidate should have 3+ years of working with ML infrastructure or LLM inference, including PyTorch internals, custom operators, and distributed AI systems.
Proficiency in C++ is required, with experience creating C++ extensions in Python.
Experience with cloud platforms, containerization, and ML Ops best practices is also necessary.
A Bachelor's degree in Computer Science, Engineering, or a related field-or equivalent practical experience-is required.
Bonus Points
A Master's or PhD in Computer Science, Engineering, or related fields can be beneficial.
Familiarity with distributed inference frameworks (NCCL, CCL, GLOO, DeepSpeed, TorchServe) and hands-on experience in CI/CD pipeline management and automating testing infrastructure can also be advantageous.
