Machine Learning Research Manager

Remote from: UK, EMEA +1 more, Denmark
Annual salary: Undisclosed
Salary information is not provided for this position. Check our Salary Directory to estimate the average compensation for similar roles.
Department: Data Science & Analytics
Employment type: Full Time,
Job posted: 17 Jun 2026
Apply before: 17 Jul 2026
Experience level: Midweight
Views / Applies: 25 / 3

About Miro

We’re an innovation workspace, built for teams that are building out the future.

Computer Software
2011

AI Summary

Miro is hiring a Machine Learning Research Manager to lead a team building the 'Intelligent Canvas'—a platform that models complex user behaviors on an infinite spatial canvas. This role involves managing applied research at the intersection of vision, language, and graph theory, working with unique multimodal and graph-based datasets. The manager will define the research roadmap, drive rapid prototyping from foundation models to production, and partner with engineering and product leadership. Candidates need a PhD or equivalent experience with 2+ years managing high-performing ML teams, deep knowledge of Transformers, Diffusion models, and GNNs, and a product-first mindset. The role offers equity, wellbeing benefits, and a learning stipend.

Role DNA

Job Complexity

Easy Hard

Pace & Pressure

Relaxed Fast-paced

Autonomy Level

Guided Full Ownership

Communication Load

Independent Highly Collaborative

AI Insight This role requires deep technical expertise across multiple advanced ML domains (Transformers, GNNs, Diffusion models) plus proven leadership in a product-driven environment, making it extremely challenging.

Salary Analysis

AI Insight The offered salary is not specified, but the market median for a Machine Learning Research Manager in the US is estimated at $250k. Compensation typically includes equity and benefits, making it highly competitive for top talent.

Key Skills

Machine Learning Deep Learning Transformers Graph Neural Networks Generative AI Research Management Team Leadership Product Strategy Multimodal Learning AI Ethics

Cover Letter Sample

Dear Hiring Manager,

I am excited to apply for the Machine Learning Research Manager role at Miro. With a PhD in Computer Science and over 5 years leading applied research teams, I have a strong track record of bridging cutting-edge ML research with product impact. At my previous company, I built and managed a team that fine-tuned large language models and graph neural networks to enhance user collaboration tools, resulting in a 30% increase in user engagement.

I am particularly drawn to Miro's vision of the 'Intelligent Canvas' and the opportunity to work with unique spatial and multimodal data. My expertise in Transformers, GNNs, and generative models aligns perfectly with the research challenges you describe. I thrive at the intersection of research and production, ensuring that scientific rigor translates into intuitive, user-centric features.

I look forward to the possibility of contributing to Miro's mission of enabling innovation through intelligent collaboration.

Sincerely, [Your Name]

Possible Interview Questions

How would you approach fine-tuning a large language model for a specific domain task like generating technical diagrams from text descriptions?

I would start by curating a high-quality dataset of text-diagram pairs, then use parameter-efficient fine-tuning (e.g., LoRA) to adapt a pre-trained model like GPT-4 or Llama. I would evaluate using both automated metrics (e.g., BLEU, ROUGE) and human evaluation to ensure diagrams are accurate and useful. I would also consider incorporating retrieval-augmented generation (RAG) to leverage existing diagram libraries.

Describe a time you had to balance a long-term research goal with short-term product deadlines. How did you manage it?

At my previous role, we had a long-term goal to build a multimodal recommendation system, but a product deadline required a quick improvement. I split the team into two tracks: one focused on a simpler heuristic model for immediate release, and another continued research on the ML model. I set clear milestones and communicated trade-offs to stakeholders, ensuring we delivered on time while progressing the research.

How do you foster a culture of scientific rigor in an applied research team?

I encourage reproducible experiments by using version control for data and code, maintaining experiment tracking with tools like MLflow, and holding regular paper reviews. I also set expectations for evaluating models not just on accuracy but on user impact and latency. Recognizing team members for rigorous work and providing time for research exploration are key.

Miro has unique spatial collaboration data. How would you model user behavior on an infinite canvas using graph neural networks?

I would represent each canvas as a graph where nodes are elements (text, images, etc.) and edges represent spatial or semantic relationships. A GNN can learn to predict user actions like object placement or grouping. I would also incorporate temporal dynamics using recurrent GNNs to model sequences of actions. This can enable features like autocomplete of diagrams or intelligent content suggestions.

Given the rapid pace of AI research, how do you decide which new papers or models to explore for potential product application?

I filter papers by relevance to our core problems (e.g., spatial reasoning, multimodal generation) and assess the maturity of the technique. I prioritize those with open-source implementations and strong empirical results. I then have a rapid prototyping phase where we test the model on a small subset of our data to gauge performance and latency. Only promising ones get integrated into the roadmap.

Miro is the online workspace for innovation, used by 100M+ people to build the next big thing. We are seeking a Research Manager to lead the team defining the brain behind the “Intelligent Canvas.”

This is not for building a standard GenAI or Recommendation engine. You will lead a team operating at the intersection of Vision, Language, and Graph Theory, working with a dataset unlike any other in the industry: spatial, unstructured, and deeply human collaboration data. You will bridge the gap between open-ended research (LLMs, Diffusion Models, GNNs) and product impact, empowering teams to dream, design, and build faster.

What You’ll Do

Build and lead a world-class applied research team, hiring and mentoring researchers who excel at the intersection of deep learning theory and production engineering.
Define the research roadmap to support the “Intelligent Canvas,” identifying opportunities to model complex user behaviors—from multi-user collaboration on an infinite canvas, to multi-format AI-powered generation (e.g. slide deck, technical diagram, web app prototypes, etc.), and more.
Pioneer research on unique spatial datasets. Unlike standard text/image corpuses, you will explore massive multimodal and graph-based datasets, uncovering how teams organize information spatially and collaborate together to solve complex multi-modal problems.
Drive the “Research-to-Product” velocity. You will create the framework for rapidly testing foundation models (e.g., GPT-4, Llama, Stable Diffusion) and fine-tuning them for specific domain tasks (e.g. prototype generation, diagram generation, mindmap generation).
Cultivate a culture of scientific rigor. Encourage the team to stay at the cutting edge (NeurIPS, CVPR) while maintaining a relentless focus on shipping features that delight users.
Partner with Engineering & Product Leadership to translate abstract AI capabilities into intuitive solutions that feel like magic to our users.
Architect organizational processes for model governance, ensuring rigorous evaluation frameworks, reproducibility, and ethical AI practices.

What We’re Looking For

Proven track record of technical leadership: 2+ years of experience managing high-performing Applied Science or ML Engineering teams in a product-led tech company or top-tier research lab.
Multimodal & GenAI Depth: You don’t just use APIs; you understand the architecture of Transformers, Diffusion models, and Graph Neural Networks (GNNs). You can guide a team through the complexities of fine-tuning and RAG at scale.
A “Product-First” Researcher: You understand that accuracy metrics (F1, AUC) are proxies, not goals. You prioritize user value and latency constraints in production.
Curiosity for the “Unsolved”: You are excited by the ambiguity of modeling “collaboration.” How do you quantify a “good brainstorm”? How do you autocomplete a flowchart?
Strategic Communication: You can articulate the difference between “hype” and “utility” to executive stakeholders and align research efforts with Miro’s long-term strategy.

Education + Experience

Option A: PhD in Computer Science, Statistics, Mathematics, or related field + 2+ years of people management experience.
Option B: Master’s degree or equivalent deep technical experience + 5+ years of industry experience in ML, including 2+ years of people management.

Bonus (Nice-to-Have)

A portfolio of research contributions, including publications in top-tier conferences (NeurIPS, ICLR, KDD) or impactful technical blog posts.
Familiarity with the modern MLOps stack and experience guiding teams in building their own research infrastructure.

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. If you would like to learn more about location-specific benefits, please refer to our Global Miro benefits board.

Recruiter: #LI-MH1

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!

Check out more about life at Miro:

Youtube: https://www.youtube.com/@lifeatmiro
Blog: https://miro.com/careers/life-at-miro/all/
Instagram: https://www.instagram.com/mirohq/

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.

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