Top Tech Jobs & Startup Jobs

Optimize and benchmark deep learning training and inference stacks on HPC/GPU hardware; profile and triage system bottlenecks; build automation tools; develop kernels and systems for new models; lead design reviews, review code, document work, and represent the lab at conferences.

Lead research to improve LLM efficiency and architectures, design and implement NLP algorithms (using PyTorch), integrate LLMs with vision models, optimize models and data pipelines, collaborate on code/design reviews, publish technical reports and papers, represent the institute at conferences, and contribute to ML infrastructure and model evaluation.

Design, extend, and maintain distributed training infrastructure: modify frameworks (DeepSpeed, FSDP, etc.), implement distributed optimizers, build multi-node launch/config systems, implement experiment tracking and monitoring, optimize reliability and performance, and write production-quality ML infra code in PyTorch or JAX.

Lead day-to-day IT operations across U.S. and UAE offices, ensure infrastructure uptime, manage endpoints and MDM, enforce security and compliance, support employee onboarding and high-touch IT service, manage assets and vendors, and coordinate global IT standards and projects to enable scalable, reliable technology operations.

Curate web-scale datasets and design data synthesis pipelines to support foundation-model training. Develop automated data quality assessment, trace data impact on model capabilities, optimize data-model co-design, and contribute to research publications and conferences.

Design and optimize communication stacks and runtime for large-scale distributed training (including MoE and hybrid parallelism). Improve collective performance, reduce latency, ensure fault-tolerant execution, debug NCCL/UCX/RDMA issues, and co-design topology-aware orchestration for 1,000+ GPU clusters.

Develop and maintain full-stack applications supporting foundation-model research. Design client- and server-side architecture, ensure responsiveness, security, and performance, collaborate with researchers/ML engineers, troubleshoot and document software, and contribute to HPC and deep-learning research outputs and publications.

Develop and implement large-scale machine learning and foundation-model systems, collaborate with research teams to integrate technologies, maintain ML lifecycle (data preprocessing, pre-/post-training, evaluation), conduct design reviews and code reviews, debug system issues, contribute to documentation and research publications, and represent the institute at conferences.

Short-term, intensive internship supporting foundation model work: prepare datasets, assist with model architecture and testing, analyze outputs for accuracy and bias, refine algorithms, and document findings for research collaboration.

Research and engineer agentic language models: develop algorithms, datasets, evaluations, and scalable RL and distributed training/serving infrastructure; publish papers and contribute open-source software.