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