Top Tech Jobs & Startup Jobs

Build and operate Eval360 evaluation service, perform deep error analysis on model outputs, design error taxonomies and review interfaces, implement front-end and back-end evaluation pipelines, dashboards, APIs, and data workflows, and collaborate with researchers and engineering teams to improve model quality and evaluation infrastructure.

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.

Lead research and engineering to improve data-centric LLM reasoning, retrieval, and interaction. Develop scalable data collection and curation pipelines, build tooling for fine-tuning, evaluation, and deployment, and collaborate on RAG, agentic systems, and knowledge retrieval. Publish results, prototype APIs/infrastructure, and drive engineering best practices in a high-performance research environment.

Develop scalable world models to simulate physical environments, optimize large-scale multimodal training and inference, build video data pipelines and annotation systems, create evaluation benchmarks, and collaborate across engineering and research teams to enable real-time, efficient model interaction.

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.

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.

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.

Provide overnight operational coverage for large-scale GPU clusters: monitor health and performance, triage incidents, support researchers, run recovery procedures, validate deployments and upgrades, track utilization, and build automation and monitoring tools.

Design, train, scale, and release industrial-scale diffusion-based large language models. Develop and evaluate training strategies and objectives for efficient model scaling, publish research, and contribute to open-source releases while collaborating with researchers and engineers across architecture, training, and infrastructure.