Jobs at ifm

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.

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.

Build and maintain large-scale data collection and preprocessing pipelines for NLP research. Develop web crawlers, APIs, and workflows; refine LLM outputs into structured datasets; collaborate with researchers to ensure data quality and document methodologies. Support scalable storage, retrieval, and distribution of datasets.

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.

Lead product vision and strategy for foundation model families (LLMs, reasoning, world models). Define user personas and roadmaps, translate research into production requirements, prioritize features, own release criteria, drive cross-functional execution, and build/scale the Product Management organization while ensuring performance and safety for public releases.

Own end-to-end agentic data pipelines and evaluation suites: source and curate trajectories and tool-use data, design RL environments and rewards, build reliable large-scale eval harnesses, run experiments, diagnose regressions, and produce research outputs and technical reports.

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.

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.

Build and productionize distributed pre-training stacks (PyTorch/JAX) at multi-node GPU scale, implement new optimizers and high-performance CUDA/Triton kernels, optimize mixed-precision training and memory/communication, develop experiment infrastructure and monitoring, and mentor junior engineers to accelerate foundational model research.

Intern will build and validate a simulator and profiling framework to model inference on NVIDIA GPUs, develop analytical GPU kernel performance models, profile kernels with Nsight tools, analyze PTX/SASS, identify bottlenecks, and recommend kernel and system-level optimizations for transformer inference on Hopper and Blackwell architectures.