Jobs at ifm

Operate and maintain the organization's IT environment, focusing on endpoint/device management, security/compliance, asset and vendor management, high-touch employee support and onboarding, office infrastructure, and acting as the IT liaison between U.S. and UAE teams to ensure operational continuity.

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.

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.

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.

Short-term intensive research internship assisting in dataset collection and preprocessing, model architecture design and tuning, implementation of testing frameworks, analysis of model accuracy and bias, algorithm refinement, and documentation to support foundation-model research.

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, build, and maintain scalable ML infrastructure for training and inference of LLM, TTS, and multimodal systems. Own model lifecycle, Kubernetes-based deployments (EKS/Helm), CI/CD, monitoring, cost optimization, infra-as-code (Terraform), and collaborate with researchers to productionize models and observability pipelines.

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.

Coordinate cross-functional work across research, engineering, product, marketing, and community for the Abu Dhabi lab. Translate priorities into project plans, track roadmaps and readiness milestones, run end-to-end model launches, liaise with MarComms and partners, maintain lightweight processes, report progress and risks to leadership, and drive resolution of blockers while improving launch and delivery processes.

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.

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.

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.