Top Tech Jobs & Startup Jobs

Develop and maintain backend services (Python/FastAPI), data pipelines, and RESTful APIs; build frontend web apps (Vue/React/Next) and Flutter mobile apps; integrate model inference and data visualization; manage PostgreSQL databases; implement CI/CD, Docker, and cloud deployment; ensure performance, security, and cross-team collaboration.

Lead research and engineering for data-centric LLM development: collect and curate web-scale datasets, build pipelines and tooling for fine-tuning, retrieval, RAG, and reasoning systems, prototype APIs and infrastructure, evaluate model performance, publish findings, and collaborate across research and engineering teams.

Manage end-to-end HPC infrastructure projects for large-scale GPU clusters: coordinate vendors and internal stakeholders, track milestones and risks, maintain documentation and governance, support compliance and export-control tracking, and produce executive reports and dashboards to ensure research timelines are met.

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.

Support the Paris IFM lab with day-to-day finance and administrative operations: payroll processing, budget monitoring, procurement, vendor relations, IT coordination, compliance with MBZUAI and French regulations, recordkeeping, travel coordination, and event support to enable researchers to focus on scientific work.

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.

Lead QA for AI-driven products, designing test plans and automation (primarily Python/PyTest), evaluating model correctness, fairness and robustness, testing distributed systems and APIs, implementing CI/CD pipelines, and collaborating with researchers to define testing metrics and quality standards.

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.

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.