研究副总裁（机器学习）
VP of Research (Machine Learning)
关于公司
我们的客户是一家由东南亚领先科技公司支持的隐秘人工智能初创公司，目前正在组建其全球创始团队。
该公司正在开发一个以人工智能为核心的通信平台，旨在通过将人工智能直接整合到对话中来简化日常任务。用户无需在多个应用程序之间切换，即可在一个智能助手内完成计划、组织、比较、研究和完成任务。
面向仍依赖传统生产力工具的数十亿用户市场，该平台专注于提供可靠的AI工作流程、持久的上下文、多步骤推理和无缝的任务执行。其使命是创造一个能显著提升生产力的AI助手，使日常工作更简单、更直观。
关于职位
我们的客户正在寻找一位机器学习副总裁（VP of Research），以定义并领导其AI平台背后的研究方向。这个领导职位将塑造AI系统在专为高频、真实使用场景设计的产品中如何推理、学习、评估和持续改进。
与产品和工程领导紧密合作，该职位将推动长期的智能战略，平衡前沿研究与实际生产影响。
主要职责
- 定义并演进平台核心AI智能的研究路线图，包括上下文表示、记忆、推理、规划和编排。
- 评估并决定何时开发专有模型架构，以及何时利用或调整前沿开源和商业AI模型。
- 设计评估框架，衡量现实世界中的性能、稳健性、安全性及长期系统行为，超越传统的基准指标。
- 作为产品开发的核心组成部分，领导公司的AI对齐、安全和防护策略。
- 推动在高级机器学习技术方面的研究和实验，包括：
- 检索增强训练（RAG）
- 专家混合模型（MoE）
- 模型蒸馏
- 多智能体编排
- 多模态AI系统
- 与产品和工程团队密切合作，定义并执行AI驱动应用的智能战略。
- 建立并维护组织内部的研究质量、技术判断和工程卓越的高标准。
- 要求
- 在设计、构建和部署机器学习系统方面有丰富经验
查看英文原文
About the Company
Our client is a stealth AI startup backed by one of Southeast Asia's leading technology companies and is currently building its global founding team.
The company is developing an AI-native communication platform designed to simplify everyday tasks by integrating AI directly into conversations. Instead of switching between multiple applications, users can plan, organize, compare, research, and complete tasks within a single intelligent assistant.
Serving a market of billions of users still relying on traditional productivity tools, the platform focuses on delivering reliable AI workflows, persistent context, multi-step reasoning, and seamless task execution. The mission is to create an AI assistant that significantly improves productivity while making everyday work simpler and more intuitive.
About the Role
Our client is seeking a VP of Research (Machine Learning) to define and lead the research direction behind its AI platform. This leadership role will shape how AI systems reason, learn, evaluate, and continuously improve within a product designed for high-frequency, real-world usage.
Working closely with product and engineering leadership, this position will drive the long-term intelligence strategy, balancing cutting-edge research with practical production impact.
Key Responsibilities
- Define and evolve the research roadmap for the platform's core AI intelligence, including context representation, memory, reasoning, planning, and orchestration.
- Evaluate and determine when to develop proprietary model architectures versus leveraging or adapting frontier open-source and commercial AI models.
- Design evaluation frameworks that measure real-world performance, robustness, safety, and long-term system behavior beyond traditional benchmark metrics.
- Lead the company's AI alignment, safety, and guardrail strategy as a core component of product development.
- Drive research and experimentation across advanced machine learning techniques, including:
- Retrieval-Augmented Training (RAG)
- Mixture of Experts (MoE)
- Model Distillation
- Multi-Agent Orchestration
- Multimodal AI Systems
- Partner closely with product and engineering teams to define and execute the intelligence strategy for AI-powered applications.
- Establish and maintain high standards for research quality, technical judgment, and engineering excellence across the organization.
Requirements
- Extensive experience designing, building, and deploying machine learning systems in production environments.
- Strong technical expertise in model behavior, failure analysis, system evaluation, and long-term AI performance.
- Proven ability to translate advanced research into reliable, production-ready AI solutions.
- Demonstrated experience making high-impact technical decisions in fast-moving and ambiguous environments.
- Deep understanding of AI evaluation methodologies, model robustness, safety, and system reliability.
- Strong ownership mindset with the ability to lead technical strategy and execution.
- Passion for building practical AI systems that deliver measurable real-world impact rather than focusing solely on academic research or benchmark performance.
Preferred Technical Skills
Experience with the following technologies is preferred:
- Python
- PyTorch and/or JAX
- GPU-based model training and inference systems
Originally posted on Himalayas