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Open nowFirst seen 4 days ago

VP of Research, Machine Learning

Hyphen Connect71 open roles

Where
Remote
Work mode
Remote
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Your applicationOpen nowVP of Research, Machine LearningHyphen Connect · Remote

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The clock on this job

Early applications get read.

12.3% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.3%7 days
  4. 19.3%14 days
  5. 35.6%30 days
  6. 58.1%60 days
This job: first seen 4 days ago

The posting

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
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