[Hiring] VP of Research (Machine Learning) @Avomind

Role Description

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.

Qualifications

• 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