VP of Research, Machine Learning

Our client is an AI startup building a proactive AI assistant that helps users complete real-world tasks through intelligent, goal-driven workflows.

As the Head of AI Research, you will define the long-term intelligence strategy behind the product. You'll lead the research direction for how AI understands context, reasons, plans, evaluates, and continuously improves in a product used frequently by real users.

Responsibilities

• Define and evolve the research roadmap for the platform's core intelligence, including context representation, memory, reasoning, planning, and agent orchestration.
• Determine when to develop new model architectures versus adapting or leveraging frontier open-source and commercial foundation models.
• Design evaluation frameworks that measure real-world usefulness, robustness, safety, and long-term system behavior—not just benchmark performance.
• Own the strategy for AI alignment, safety, and guardrails as core product capabilities.
• Lead the exploration and application of advanced AI techniques, including: Retrieval-augmented training (RAT), Mixture-of-Experts (MoE), Model distillation, Multi-agent orchestration and Multimodal AI systems.
• Partner closely with product, machine learning, and engineering teams to shape the product's intelligence roadmap and long-term technical vision.
• Set a high standard for research quality, technical judgment, experimentation, and engineering excellence across the organization.

Requirements

• Extensive experience building, deploying, or evolving machine learning systems in production.
• Deep understanding of modern machine learning, large language models, reasoning systems, and AI architectures.
• Strong technical judgment around model behavior, failure modes, and long-term architectural trade-offs.
• Proven ability to translate research into practical, production-ready AI systems.
• Comfortable making high-impact technical decisions in fast-moving, ambiguous environments.
• Strong focus on evaluation, correctness, safety, and long-term system performance.
• A builder's mindset with a high degree of ownership—you care about delivering systems that work reliably in the real world, not just advancing research.
• Operates like a founder: proactive, accountable, and driven to solve difficult technical problems from first principles.
• Technologies and tools: Python, PyTorch / JAX, GPU-accelerated training and inference systems