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.

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.

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.

Provide local finance and administrative coordination for IFM Sunnyvale: collect, review, organize, and track invoices, receipts, credit card bills, vendor records, and reimbursements; liaise with HQ Finance; maintain trackers, vendor files, contracts, and simple financial summaries; support banking/platform setup and general office administration.

Conduct and prototype novel reinforcement learning research for foundation models, including massive-scale self-play, end-to-end engineering from data curation to production, contribute to training/inference frameworks, publish and present results, engage with open-source and external collaborators.