Top Tech Jobs & Startup Jobs

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.

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.

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.

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.

Drive research and engineering to develop PAN world models: scalable training on large clusters, video data pipelines and annotation, evaluation benchmarks, visual tokenization, and inference optimization for real-time interaction.

Rapidly collect, curate, and preprocess high-quality NLP datasets. Build and maintain web crawlers, APIs, automated workflows, and scalable data pipelines. Refine and evaluate LLM outputs, ensure data quality and performance, document methodologies, and collaborate with researchers and engineers to deliver data for model training and benchmarking.

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 and implement scalable machine learning models and infrastructure for foundation models; collaborate with research teams; handle model lifecycle (data preprocessing, pre-training, evaluation); perform design and code reviews; debug system issues; contribute documentation and research papers; represent the institute at conferences.