VP of Research, Machine Learning
# VP of Research, Machine Learning **DNA325** · United States · `Remote` · `Full Time` 💼 **Уровень роли:** `Executive` 🕒 **Статус:** *Опубликовано: сегодня* · *Источник: Himalayas (JSON API)* --- ### Top Skills & Match 🎯 **Ключевой стек роли:** `[Research-Director]` `[Director-Of-AI-ML-Research]` `[VP-Of-Research]` `[Executive-AI-Research-Director]` `[VP-Of-Machine-Learning]` `[VP-Of-AI-And-Machine-Learning]` `[Director-AI-ML-Research]` `[VP-Of-AI-ML]` --- ### About the Role Our client is an AI-native product company built to replace how billions of people manage their digital lives — starting with email, notes, and task tools that were never designed to be AI-native. It's backed by multi-million-dollar investment and built remote-first from day one, with a clear target: cut the time it takes users to get everyday things done by roughly 90%. That means solving the problems most AI products avoid — long-running workflows, persistent context, and reliable behavior under real-world, non-deterministic conditions. The team is small and high-talent-density by design, not by necessity, and moves at a pace that matches the scale of what it's building. About the Role You'll own the research and intelligence direction of this system — defining how the AI reasons, evaluates, and improves in a product used with high frequency. What You'll Do - Set and evolve the research direction for the company's core intelligence, including context representation, memory, reasoning, planning, and orchestration - Decide when to design new model architectures versus adapting or leveraging frontier open-source or commercial models - Define evaluation frameworks that measure real-world usefulness, robustness, safety, and long-term behavior — not benchmark vanity - Own alignment, safety, and guardrail strategy as first-class product concerns - Guide exploration of frontier techniques: retrieval-augmented training, mixture-of-experts, distillation, multi-agent orchestration, multimodal systems - Shape early product intelligence direction in close partnership with product and application engineering - Set the technical bar for research rigor, judgment, and taste across the organization Requirements - Deep experience building or evolving real machine learning systems used in production - Strong technical judgment around model behavior, failure modes, and long-horizon trade-offs - A builder's mindset — you care about systems that work in the real world, not just ideas - Comfortable making irreversible or high-impact decisions with incomplete information - An obsession with evaluation, correctness, and how systems behave over time - High ownership mentality — you operate as a founder, not a manager This probably isn't a fit if your focus is primarily publishing, incremental benchmarks, or managing a large research organization — this role is not that. Tech Stack Python · PyTorch / JAX · GPU-based training and inference systems What to Expect The best products in the world are built by small, world-class teams. Decisions are made collectively, at rapid speed — balancing high-quality shipping with fast learning. You'll be expected to bring structure, exercise judgment, and execute independently. Compensation and benefits are competitive and vary by location; the package includes base salary and equity, discussed openly with you as part of the process. If there's a fit, expect 3–4 interviews total, followed by a prompt, transparent decision. Originally posted on Himalayas
- Research-Director
- Director-Of-AI-ML-Research
- VP-Of-Research
- Executive-AI-Research-Director
- VP-Of-Machine-Learning
- VP-Of-AI-And-Machine-Learning
- Director-AI-ML-Research
- VP-Of-AI-ML
Наблюдалась 2026-10-17, впервые 2026-10-17, источник — Himalayas (JSON API).