At a Glance
- Tasks: Lead innovative research in Machine Learning to shape the future of collaboration.
- Company: Join Miro, a dynamic visual workspace for innovation with a global reach.
- Benefits: Enjoy competitive equity, health insurance, and a supportive work environment.
- Why this job: Make a real impact by pioneering cutting-edge AI technologies in a collaborative team.
- Qualifications: PhD or equivalent experience in Computer Science, Math, or Physics required.
- Other info: Be part of a diverse team that values creativity and collaboration.
The predicted salary is between 54000 - 90000 £ per year.
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." Your challenge is unique in the industry: You are not just processing text. You are building models that understand spatial relationships, visual diagrams, and unstructured collaboration. You will research, prototype, and ship novel architectures that combine Large Language Models (LLMs), Computer Vision, and Graph Neural Networks (GNNs) to make Miro the smartest collaboration platform on earth.
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
- LON: Competitive equity package
- Health insurance for you and your family
- Corporate pension plan
- Lunch, snacks and drinks provided in the office
- Wellbeing benefit and WFH equipment allowance
- Annual learning and development allowance to grow your skills and career
- Opportunity to work for a globally diverse team
Multi Location: Amsterdam / Berlin / Yerevan / London: Competitive equity package, Lunch, snacks and drinks provided in the office, Wellbeing benefit and WFH equipment allowance, Annual learning and development allowance to grow your skills and career, Opportunity to work for a globally diverse team.
About Miro
Miro is a visual workspace for innovation that enables distributed teams of any size to build the next big thing. The platform's infinite canvas enables teams to lead engaging workshops and meetings, design products, brainstorm ideas, and more. Miro, co-headquartered in San Francisco and Amsterdam, serves more than 100M users and 250,000 companies collaborate in the Innovation Workspace. Miro was founded in 2011 and currently has more than 1,600 employees in 13 hubs around the world.
We are a team of dreamers. We look for individuals who dream big, work hard, and above all stay humble. Collaboration is at the heart of what we do and through our work together we hope to create a supportive, welcoming, and innovative environment. We strive to play as a team to win the world and create a better version of ourselves every day. If this sounds like something that excites you, we want to hear from you.
At Miro, we strive to create and foster an environment of belonging and collaboration across cultural differences. Miro's mission — Empower teams to create the next big thing — is how we think about our product, people, and culture. We believe that creating big things requires diverse and inclusive teams. Diversity invites all talent with different demography, identities and styles to step in, and inclusion invites them to step closer together. Every day, we are working to build a more diverse Miro, cultivate a sense of belonging for future and current Mironeers around the world, and foster an environment where everyone can collaborate and embrace differences.
Lead Research Scientist in London employer: Miro
Contact Detail:
Miro Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Lead Research Scientist in London
✨Tip Number 1
Network like a pro! Reach out to people in your field on LinkedIn or at industry events. A friendly chat can lead to opportunities that aren’t even advertised yet.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to Machine Learning and AI. This gives potential employers a taste of what you can do.
✨Tip Number 3
Prepare for interviews by practising common questions and scenarios specific to the role. Think about how you’d tackle real-world problems they might throw at you.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive!
We think you need these skills to ace Lead Research Scientist in London
Some tips for your application 🫡
Show Off Your Expertise: When you're writing your application, make sure to highlight your deep research expertise and any relevant publications. We want to see how your experience aligns with the cutting-edge work we're doing at Miro!
Be Clear and Concise: Keep your application straightforward and to the point. We appreciate clarity, so avoid jargon unless it's necessary. Remember, we want to understand your thought process and technical strategy without getting lost in complex language.
Tailor Your Application: Make sure to customise your application for the Lead Research Scientist role. Highlight your experience with multimodal inputs and any specific projects that showcase your ability to bridge theory and production. This will help us see how you fit into our vision!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows us you’re keen on joining the Miro team!
How to prepare for a job interview at Miro
✨Know Your Stuff
Make sure you’re up to speed with the latest research in Machine Learning, especially around LLMs, GNNs, and Computer Vision. Brush up on recent papers from NeurIPS or CVPR, and be ready to discuss how they could apply to Miro's Intelligent Canvas.
✨Showcase Your Projects
Prepare a portfolio that highlights your past work, especially any custom architectures you've designed or models you've shipped. Be ready to explain the challenges you faced and how you overcame them, as this will demonstrate your problem-solving skills.
✨Communicate Clearly
Practice explaining complex concepts in simple terms. You’ll need to convey your ideas to non-technical team members, so being able to articulate the 'why' behind your decisions is crucial. Think of examples where you’ve done this successfully in the past.
✨Ask Insightful Questions
Prepare thoughtful questions about Miro’s current projects and future direction. This shows your genuine interest in the role and helps you understand how you can contribute to their mission of empowering teams to innovate.