Jobs at ifm

Own and maintain a high-performance website while creating visual assets, diagrams, infographics, presentation materials, and short-form videos. Monitor website performance, security, accessibility, SEO, and analytics. Collaborate with marketing, editorial, social media, and external design partners to maintain a consistent visual identity and translate complex AI research into clear, engaging content across platforms.

Design, develop, and operate state-of-the-art systems for curating, ingesting, filtering, annotating, and training on billion-scale visual data. Research areas include motion scoring, camera control, and video understanding. The role converts human data-curation efforts into durable, automated systems with quantitatively verifiable performance, contributing to foundation model and world model development.

Build and grow a developer and user community around foundation models, APIs, and tools. Plan workshops, hackathons, webinars, and training; develop external partnerships; represent the institute at events; and track community impact. Translate technical research into documentation, tutorials, demos, and integrations. Provide technical onboarding and support, gather user feedback for product roadmaps, and coordinate with research, product, engineering, DevOps, MLOps, and communications teams to drive model adoption.

Lead editorial vision and content strategy for a technical AI research lab. Develop the editorial voice, calendar, content engine, long-form and short-form content, multimedia storytelling, and owned channels. Translate complex research into credible, accessible communications for technical and general audiences. Partner with engineers, researchers, communications, and product teams on launches and flagship reports; manage a social media manager, build a freelance writing team, and track content performance metrics.

Manage and grow social media channels for an AI research lab, engaging developer and research communities across X, Hugging Face, Reddit, LinkedIn, Instagram, and YouTube. Responsibilities include channel strategy, launch amplification, community building, content creation, brand voice management, trend monitoring, content calendars, cross-functional execution, and analytics reporting.

Develop and execute research communications strategies for an AI research institute. Translate complex work involving foundation models, AI systems, papers, benchmarks, open-source projects, and scientific milestones into compelling narratives. Advise leaders and researchers on positioning, messaging, launches, stakeholder engagement, media opportunities, conferences, and external reputation. Build the institute’s profile through influential media, speaking engagements, podcasts, and technical communities while anticipating reputational, technical, and competitive considerations.

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, 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.