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.
Stage: Early-stage AI startup Focus: Proactive AI assistant for everyday productivity Work mode: Remote-first, distributed team