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What you’ll be doing:
Develop algorithms for AI/DL, data analytics, machine learning, or scientific computing
Tackle large-scale distributed systems capable of performing end-to-end AI training and inference-deployment (data fetching, pre-processing, orchestrate and run model training and tuning, model serving)
Analyze, influence, and improve AI/DL libraries, frameworks and APIs according to good engineering practices
Research, prototype, and develop effective tools and infrastructure pipelines
Publish innovative results on Github and scientific publications
What we need to see:
A PhD or Master's Degree (or equivalent experience) and 8+ years of industry experience in Computer Science, AI, Applied Math, or related field
Strong Mathematical fundamentals and AI/DL algorithms skills or experience
Excellent programming, debugging, performance analysis, test design and documentation skills
Experience with AI/DL Frameworks (e.g. PyTorch, JAX)
Excellent C/C++ and Python programming skills
Ways to stand out from the crowd:
Knowledge of GPU/CPU architecture and related numerical software
Prior experience with Generative AI techniques applied to Large Language Modelsand multimodal learning (Image, Video, Speech etc.)
Exposure to large-scale AI training, understanding of the compute system concepts (latency/throughput bottlenecks, pipelining, multiprocessing etc) and related performance analysis and tuning
Hands-on experience with inference and deployment environments would be an asset (e.g. TRT, ONNX, Triton)
You will also be eligible for equity and .
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