Top Tech Jobs & Startup Jobs

Design and build post-training infrastructure for supervised fine-tuning, reinforcement learning, and model evaluation. Develop reproducible pipelines for data preparation, versioning, sampling, training, checkpointing, and experiment tracking. Integrate systems with partner training stacks, model APIs, compute environments, and evaluation platforms. Run controlled experiments to measure dataset impact on model behavior, analyze noisy results, identify confounders, and produce actionable conclusions.

Own planning and execution of technical research programs spanning post-training, enterprise initiatives, fellowships, evaluations, and partner research. Translate ambiguous objectives into milestones, owners, dependencies, success criteria, and decision points. Track experiment progress, risks, blockers, partner dependencies, and releases while aligning researchers, engineers, program leads, and external partners. Apply working knowledge of LLM development, datasets, supervised fine-tuning, reinforcement-learning post-training, and evaluation.

Own expert support for a frontier-AI expert network: manage queues and SLAs, resolve payment and project disputes, document and surface trends, build playbooks and escalation policies, and partner with operations and growth to reduce support volume and improve contributor experience.

Lead GTM/RevOps execution: own CRM and pipeline hygiene, analytics and forecasting, RFIs, contract-to-cash, sales-stack automation, follow-ups, and customer engagement cadences to scale revenue operations and reduce manual work.

Lead supply-side growth for a talent marketplace: design sourcing strategy, build scalable acquisition channels and automation with engineering, forecast supply, optimize marketplace operations, recruit and manage a growth team, and transform manual playbooks into productized systems that increase contributor quality and retention.

Design datasets, evaluation rubrics, and reward signals for RLHF/RLVR; build real and synthetic data pipelines; run experiments modeling annotator behavior; develop quantitative metrics for dataset quality, diversity, and downstream impact; partner with research teams to translate training objectives into data and evaluation specs.

Partner with the CEO and operations team to run special projects end-to-end, build operational capabilities (billing, revenue ops, BD support), gather and analyze qualitative and quantitative data, support recruiting and administrative finance/office functions, identify and fix operational inefficiencies, and document and hand off durable processes.

Own and build People programs including compensation strategy, leveling and career pathways, performance management, learning & development, retention measurement, and org design. Partner with founders and finance to model comp/headcount tradeoffs, instrument programs as products, and iterate using data for an AI-native workforce.

Lead and deliver high-impact dataset and data-solution projects for frontier AI customers. Manage client relationships, coordinate specialist engineering and research teams, design dataset specifications, scope and execute projects end-to-end, and support cross-functional operational initiatives to drive revenue and product development.

Manage and deliver high‑impact dataset and data‑solution projects for frontier AI labs. Scope, coordinate expert contributors, design dataset specifications, write production Python code, and drive projects end‑to‑end to influence foundation model training and product outcomes.