Member of Technical Staff, Enterprise AI, Remote- Full-time
# Member of Technical Staff, Enterprise AI, Remote- Full-time **Xperteez Technology** · United States · `Remote` · `Full Time` 💼 **Уровень роли:** `Senior` 💰 **Компенсация:** `$300,000 – $700,000` 🕒 **Статус:** *Опубликовано: сегодня* · *Источник: Himalayas (JSON API)* --- ### Top Skills & Match 🎯 **Ключевой стек роли:** `[Member-Of-Technical-Staff]` `[Research-Scientist]` `[Applied-Research-Scientist]` `[Machine-Learning-Engineer]` `[AI-Research-Engineer]` `[Sr.-Staff-AI-Engineer]` `[Staff-Applied-AI-Engineer]` `[Staff-AI-Software-Engineer]` `[Staff-AI-Engineer]` `[Enterprise-AI-Engineer]` `[Sr.-Staff-Machine-Learning-Engineer]` `[Staff-Machine-Learning-Engineer]` `[Staff-ML-Engineer]` --- ### About the Role Job Title: Member of Technical Staff, Enterprise AI Job Type: Full-time Location: Remote Required Skills: - Research Signal Judgment - ML-Oriented Data Design - Ops-to-Research Translation - Enterprise AI The Role: As a Member of Technical Staff, you will function as a forward-deployed research partner embedded directly within enterprise AI systems. You will work on live workflows, uncover real-world failure modes, and drive rapid experimental cycles to improve system performance. What You’ll Do: - Embed within enterprise AI workflows as a research collaborator, working alongside domain experts and client teams. - Surface, formalize, and prioritize system failure modes in real-world deployments. - Design high-signal datasets and evaluation protocols to target identified weaknesses. - Run tight experimental loops to validate hypotheses and quantify improvements. - Produce clear, decision-oriented analyses of system behavior and performance. - Develop and benchmark agentic workflows, focusing on robustness and scalability. - Build lightweight tooling to support evaluation, data curation, and rapid iteration. - Contribute to internal and external research artifacts, including reports and benchmarks. Who You Are: - Master’s degree in Computer Science, Machine Learning, Artificial Intelligence, or a related technical field. - Strong judgment for research signal quality, including data selection and evaluation design. - Experience designing datasets and evaluation frameworks for ML systems. - Ability to translate ambiguous operational issues into structured research problems. - Familiarity with RL environments and/or agentic system evaluation. - Clear, concise communicator with a bias toward actionable insight. - Proven ability to execute in fast iteration cycles and high-ambiguity settings. - Collaborative mindset with experience working across research, product, and domain teams. Preferred: - Strong client-facing experience, particularly in technical or research-driven environments. - Experience building internal research or evaluation tooling. - Contributions to benchmarks, research publications, or open research initiatives. - Exposure to enterprise AI deployments or forward-deployed research models. Originally posted on Himalayas
- Member-Of-Technical-Staff
- Research-Scientist
- Applied-Research-Scientist
- Machine-Learning-Engineer
- AI-Research-Engineer
- Sr.-Staff-AI-Engineer
- Staff-Applied-AI-Engineer
- Staff-AI-Software-Engineer
- Staff-AI-Engineer
- Enterprise-AI-Engineer
- Sr.-Staff-Machine-Learning-Engineer
- Staff-Machine-Learning-Engineer
- Staff-ML-Engineer
Наблюдалась 2026-10-12, впервые 2026-10-12, источник — Himalayas (JSON API).