Top Tech Jobs & Startup Jobs

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.

Design, develop, and operate high-performance distributed software for large-scale AI/ML training. Optimize kernel and accelerator code, tune communication libraries (NCCL, MPI, UCX, RCCL, RDMA), support ML frameworks (PyTorch, MegatronLM, DeepSpeed), and contribute to scheduling/orchestration (Slurm, Kubernetes). Collaborate with researchers, hardware vendors, and open-source communities to debug and improve performance across the stack.

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 research on efficient large language models and multimodal integration. Design, implement, and optimize NLP algorithms and training; collaborate across teams, write technical reports and papers, and represent the institute at conferences.

Build and maintain scalable multimodal (primarily video) data pipelines, support large-scale distributed training and inference optimization for world and video generative models, integrate research prototypes into production, ensure system reliability, and contribute to evaluation, documentation, and research outputs.

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.

Build and grow a developer and user community around IFM foundation models; run events and trainings; translate research into docs, demos, and onboarding; support external adopters and gather feedback to inform product and research roadmaps.

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.

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.