VP of Research (Machine Learning)

Description

• Our client, an early-stage AI company, is hiring a VP of Research (Machine Learning) to lead the intelligence and research direction behind a proactive AI assistant. Billions of people still run their day-to-day through tools that weren’t built with AI in mind – inboxes, notes, to-do lists. This product aims to change that, cutting the time people spend on routine tasks by roughly 90% through reliable, multi-step, tool-using AI. You’d be the person deciding how the system reasons, learns, and gets evaluated, on a product already used at high frequency.

Responsibilities

• Chart the research roadmap across memory, context handling, reasoning, planning and orchestration for the assistant’s core intelligence
• Call the shots on building custom model architecture vs. adapting existing open-source or commercial frontier models
• Build out evaluation systems that capture real-world reliability and safety, not just benchmark scores
• Treat alignment, safety and guardrails as core product decisions, not an afterthought
• Push technical exploration into areas like retrieval-augmented training, mixture-of-experts, distillation, multi-agent setups and multimodal input
• Work hand-in-hand with product and engineering to shape what the assistant can do early on

Requirements

• Fluent in Python and PyTorch/JAX, comfortable running GPU training and inference at scale

• A track record of shipping or evolving ML systems that run in production, not just research prototypes

• Sharp instincts for how models fail, behave, and hold up over long time horizons

• A hands-on builder who cares more about what works in the real world than what’s theoretically elegant

• Able to make high-stakes calls with incomplete information, and live with them

• Genuinely obsessed with evaluation and correctness – how the system behaves today and months from now

• Operates like a founder: full ownership, not delegation

• Nice to have

• Has taken a research function from zero to one inside a startup before

• Practical experience with retrieval-augmented training, MoE, distillation, multi-agent systems or multimodal models

• Has personally owned safety/guardrail strategy for a product already live with users

• What they offer

• Cash and equity compensation

• Remote-first setup with flexible hours as part of a distributed, global team

• Generous paid time off

• Company laptop provided

• A quick hiring process – 3, occasionally 4, interviews, with fast decisions afterwards

About the company

They’re an early-stage AI company building a proactive assistant aimed at the 5+ billion people currently stuck using non-AI-native tools for everyday things – email, notes, tasks. The focus is squarely on reliability: long-running workflows, persistent context, and tasks that actually get done, even though the underlying models aren’t fully deterministic.