Found.dev
Role Highlights
Languages used
CUDA
Triton
Python
Key skills
Deep Learning
ML Ops
Model Optimization
CICD
Distributed Systems
Technical Leadership
Site Reliability
Design Thinking
AI Engineer
LLMs
Infrastructure
Cloud
Automation
Security
Inference
Backend
Machine Learning
Transformers
Operations
Debugging
Deployment
Kernel
Microservices
Architecture
Tools, Libraries and Frameworks
HuggingFace
Rest
IOS
IBM
GPUs
TensorBoard
Docker
Kubernetes
Linux
Flash Attention
PyTorch
Tensorflow
Description
The role involves enabling and optimizing Large Language Models for training and inference on specific hardware platforms. The engineer will drive performance improvements for production workloads by focusing on latency, throughput, and memory efficiency. Responsibilities include implementing model optimizations, evaluating functional correctness, and building scalable distributed training systems. The position requires developing high-performance kernels and profiling systems to resolve compute and memory bottlenecks. Additionally, the role entails maintaining infrastructure for stable deployments and collaborating with various technical teams to contribute to the broader software ecosystem.
Required Qualifications and Skills
The role requires at least five years of experience in AI/ML systems, deep learning, or performance engineering. Candidates must possess strong programming proficiency in Python and working knowledge of C++. Necessary technical expertise includes a deep understanding of PyTorch internals, LLM architectures, and tensor operations. Furthermore, the position requires experience with distributed training frameworks, profiling tools, and containerization within Linux environments.
Disclaimer
Disclaimer: Job and company description information and some of the data fields may have been generated via GPT-4 summarisation and could contain inaccuracies. The full external job listing link should always be relied on for authoritative information.
About the company
IBM
Description
IBM infuses core business operations with intelligence, from machine learning to generative AI, to make organizations more responsive, productive, and resilient. It helps clients put AI into action now, creating real value with trust, speed, and confidence across various areas like digital labor, IT automation, and security. The ability to utilize all data is critical, as AI's effectiveness is dependent on the quality of data fueling it, with IBM's AI, and data platform aiming to scale and accelerate AI's impact with trusted data. IBM's hybrid cloud platform offers a comprehensive approach to development, security, and operations across hybrid environments, laying a flexible foundation for leveraging data wherever it resides.
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