VP of Research (Machine Learning)

Description

Full Job Description

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

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