Miro is looking for a Lead Research Scientist to serve as the technical "North Star" for our Machine Learning organization. You will operate as a high-level Individual Contributor (Staff/Principal level), driving the architectural decisions behind the "Intelligent Canvas."
What You’ll Do
- Pioneer Novel Architectures: Move beyond off-the-shelf APIs. You will design and train custom architectures that fuse multimodal inputs (text, sketches, diagrams, screenshots, code, etc.) into a unified representation of user intent.
- Bridge Theory and Production: You will read the latest papers (NeurIPS, ICLR, CVPR) on Monday and have a working prototype by Friday. You bridge the gap between academic theory and scalable, low-latency production systems.
- Define the Technical Strategy: While Managers define what we build, you define how we build it. You will make high-stakes decisions on model selection (e.g., Diffusion vs. Autoregressive), build vs. buy, and fine-tuning strategies (LoRA, Q-LoRA).
- Mentorship & Technical Excellence: You will elevate the entire ML research engineering organization by conducting rigorous code reviews, hosting paper reading groups, and mentoring research engineers on mathematical fundamentals and experimental design.
- Solve the "Unsolved": You will tackle ambiguous problems with no StackOverflow answers—such as "How do we generate a valid UML diagram from a rough sticky-note brain dump?" or "How do we detect 'agreement' in a spatial cluster of comments?"
What You’ll Need
- Deep Research Expertise: PhD or equivalent deep industrial experience in Computer Science, Math, or Physics. You have a track record of publishing in top-tier conferences or shipping models that serve millions of users.
- Public Track Record: A portfolio of patents, impactful open-source contributions, or first-author publications in top-tier conferences (NeurIPS, ICML, CVPR, ICLR).
- Mastery of the Modern Stack: You are an expert in PyTorch or JAX. You can implement complex loss functions from scratch and debug distributed training issues on massive GPU clusters.
- Specialization in Structure & Generation: Deep experience in at least one of the following: Generative AI (LLMs/Diffusion), Graph Neural Networks (GNNs), or Geometric Deep Learning. You understand how to model relationships, not just tokens.
- Engineering Rigor: You write clean, modular, production-ready code. You understand the trade-offs between model accuracy and inference latency.
- Communication: You can explain the "Why" behind complex mathematical concepts to Product Managers, Designers, and Executives, turning abstract research into a compelling product vision.
Education + Experience
- Option A: PhD in Computer Science, Machine Learning, Mathematics, Physics, or related field plus 4+ years of professional experience shipping ML at scale.
- Option B: Master’s degree or equivalent deep technical experience plus 7+ years of industry experience, with at least 2 years operating at a Senior or Staff level (driving technical strategy).
Bonus (Nice-to-Have)
- Graph Learning Expertise: Specific experience with Graph Neural Networks (GNNs) or Geometric Deep Learning. You understand how to apply ML to non-Euclidean data (like the node-edge relationships on a Miro canvas).
- Generative Media: Hands-on experience building or fine-tuning Diffusion models for image/video generation or Multimodal LLMs (vision + text).
- Performance Optimization: Experience porting models to constrained environments (e.g., ONNX, WebGpu, CoreML) or optimizing inference for real-time interaction in the browser.
- Domain Knowledge: Previous work in Computational Creativity, HCI (Human-Computer Interaction), or building tools for thought (e.g., knowledge graphs, whiteboarding tools).
What's In It For You
We want you to feel supported, connected, and ready to grow. Our global benefits package generally includes equity, a wellbeing benefit, a WFH equipment allowance, and an annual Learning & Development stipend. Join a diverse team where you can do your best work. Full benefits may differ per location.
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