Top Tech Jobs & Startup Jobs

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.

Build and improve high-quality LLM training datasets across pre-training, mid-training, and post-training. Responsibilities include data extraction, cleaning, filtering, deduplication, transformation, quality control, synthetic data generation, data evaluation, model experiments, and research into datasets and processing methods. Collaborate with researchers and engineers on foundation model projects while supporting inference, evaluation, and training experiments.

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.