VP of Research, Machine Learning
Summary
Executive ML research role setting the AI intelligence direction (memory, reasoning, planning, orchestration, evaluation, safety) for an AI-native productivity product replacing email/notes/task tools. Day to day: own research strategy and decide between new architectures vs frontier models. Stack: Python, PyTorch/JAX, GPU training and inference.
Our client is an AI-native product company built to replace how billions of people manage their digital lives — starting with email, notes, and task tools that were never designed to be AI-native. It's backed by multi-million-dollar investment and built remote-first from day one, with a clear target: cut the time it takes users to get everyday things done by roughly 90%. That means solving the problems most AI products avoid — long-running workflows, persistent context, and reliable behavior under real-world, non-deterministic conditions. The team is small and high-talent-density by design, not by necessity, and moves at a pace that matches the scale of what it's building.
About the Role
You'll own the research and intelligence direction of this system — defining how the AI reasons, evaluates, and improves in a product used with high frequency.
What You'll Do
Set and evolve the research direction for the company's core intelligence, including context representation, memory, reasoning, planning, and orchestration
Decide when to design new model architectures versus adapting or leveraging frontier open-source or commercial models
Define evaluation frameworks that measure real-world usefulness, robustness, safety, and long-term behavior — not benchmark vanity
Own alignment, safety, and guardrail strategy as first-class product concerns
Guide exploration of frontier techniques: retrieval-augmented training, mixture-of-experts, distillation, multi-agent orchestration, multimodal systems
Shape early product intelligence direction in close partnership with product and application engineering
Set the technical bar for research rigor, judgment, and taste across the organization
Requirements
Deep experience building or evolving real machine learning systems used in production
Strong technical judgment around model behavior, failure modes, and long-horizon trade-offs
A builder's mindset — you care about systems that work in the real world, not just ideas
Comfortable making irreversible or high-impact decisions with incomplete information
An obsession with evaluation, correctness, and how systems behave over time
High ownership mentality — you operate as a founder, not a manager
This probably isn't a fit if your focus is primarily publishing, incremental benchmarks, or managing a large research organization — this role is not that.
Tech Stack
Python · PyTorch / JAX · GPU-based training and inference systems
What to Expect
The best products in the world are built by small, world-class teams. Decisions are made collectively, at rapid speed — balancing high-quality shipping with fast learning. You'll be expected to bring structure, exercise judgment, and execute independently.
Compensation and benefits are competitive and vary by location; the package includes base salary and equity, discussed openly with you as part of the process.
If there's a fit, expect 3–4 interviews total, followed by a prompt, transparent decision.
