Miro is looking for a Machine Learning Research Engineer to serve as the technical "North Star" for our Machine Learning organization. You will operate as an Individual Contributor, 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

• Design, train, and ship production-grade ML models—including deep learning, NLP, and computer vision systems—that solve complex business problems and power core product features.
• Conduct deep exploratory research on massive datasets to uncover novel patterns in user behavior and content creation, translating raw data insights into new predictive modeling opportunities.
• Apply advanced fine-tuning strategies (e.g., PEFT, LoRA) to adapt state-of-the-art foundation models to specific domain tasks, rigorously experimenting to maximize performance.
• Architect scalable ML pipelines for data processing, feature engineering, training, and evaluation, ensuring high data quality and system reliability.
• Optimize model performance for latency, throughput, and resource utilization, balancing model complexity with production constraints (e.g., overfitting vs. underfitting, compute efficiency).
• Collaborate cross-functionally with data engineers, product managers, and software engineers to translate business requirements into technical ML specifications and integrate models into user-facing applications44.
• Champion MLOps excellence by automating deployment workflows, implementing CI/CD for ML, and establishing robust monitoring for model drift and health.
• Stay at the forefront of ML research, evaluating novel algorithms and techniques (e.g., Transformer architectures, quantization) to drive innovation and technical strategy.

What you’ll need

• Strong foundation in ML theory and statistics, including hypothesis testing, probability distributions, regression, classification, and optimization techniques.
• Solid engineering fundamentals. You are comfortable writing production-level Python and have a deep understanding of data structures, algorithms, and distributed system design.
• Deep proficiency in Python and the modern ML stack, with hands-on experience using libraries like Pandas, NumPy, Scikit-learn, and deep learning frameworks (PyTorch, TensorFlow).
• Gradient Debugging: Expertise in PyTorch or JAX, including experience with distributed training (e.g., DDP, FSDP) and debugging complex gradient issues.
• Applied Research: Ability to read, implement, and improve upon the latest academic papers (NeurIPS, ICML, CVPR). You don't just use libraries; you understand the math underneath them and can reproduce results in peer-reviewed papers.
• Track record of end-to-end ML delivery, from exploratory data analysis (EDA) and feature engineering to training, validation, and deploying models in a production environment.
• Experience with large-scale systems, capable of designing resilient architectures that handle vast datasets and high-throughput inference requests.
• Strong engineering mindset, valuing code quality, testing, modularity, and maintainability just as highly as model accuracy.

Education + Experience

• Option A: Bachelor's or Master's degree in Computer Science, Mathematics, Statistics, or related field plus ~3+ years of professional ML engineering experience.
• Option B: No formal degree, ~6+ years of industry experience demonstrating equivalent proficiency in building and shipping ML systems.

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

Recruiter: #LI-MH1

About the Team

Miro is on a mission to empower teams to create the next big thing, and our teams are at the forefront of making that vision a reality. Our teams drive AI-powered innovation across Miro's platform by building robust technical foundations, delightful product features, and intelligent capabilities that transform how users interact with content on the canvas. Whether developing structured widget capabilities that help users visualize and interact with complex information, building AI platform infrastructure, or creating intelligent features that anticipate user needs, our work is foundational to Miro's evolution.

About the Role

As an AI Engineer, you'll design and build AI-driven capabilities that enhance how users create, transform, and collaborate on canvas. You'll bridge AI innovation and product delivery - shipping delightful, safe, and reliable experiences that leverage cutting-edge LLMs while maintaining the performance and polish users expect from Miro's collaborative canvas.

What you’ll do

• Design, develop, and ship AI-driven capabilities—including agents, intelligent assistants, context-aware prediction, and generative AI—using Python, top-tier LLMs, TypeScript, React, Canvas, and WebGL
• Engineer high-performing AI workflows leveraging LLMs (OpenAI, Anthropic Claude, Google Gemini), prompt engineering, and generative techniques—ensuring reliability, scalability, and maintainability
• Execute end-to-end AI projects from conceptualization and prototyping to production code, collaborating closely with product managers, designers, and engineers
• Experiment and iterate on AI capabilities within your team domain, staying ahead of emerging AI tools, frameworks, and techniques
• Champion best practices in AI system development—including evaluations, LLMOps, CI/CD, observability, and ethical AI principles
• Measure and optimize AI performance, establishing benchmarks for user engagement, model accuracy, latency, error rate, and drift
• Write clean, well-tested, maintainable code, participating in code reviews and® technical discussions across the stream
• Troubleshoot and resolve production issues, ensuring high availability and performance

What you’ll need

• 3+ years of professional experience with a proven track record delivering AI-first capabilities in customer-facing products—driving value through features users directly interact with
• Excellent communication skills, able to translate complex technical concepts for non-technical stakeholders
• Strong Python skills and familiarity with FE technologies (e.g. Typescript, React etc)
• Demonstrated expertise in prompt engineering and context engineering using top-tier LLMs (OpenAI GPT, Anthropic Claude, Google Gemini)
• Ability to develop high-quality, production-ready code within complex codebases
• Experience with LLM evaluation frameworks and a track record of building evals that meaningfully inform model selection, prompt iteration, or feature quality.
• Solid understanding of ML fundamentals, including probabilistic modeling, evaluation techniques, and deep neural networks
• Experience with LLMOps and cloud services (AWS, Azure, Amazon Bedrock, etc.)
• AI-First Proficiency. We’re building for an AI-first future, and we’re looking for graduates who aren’t just using AI—they’re mastering it. We’re excited to meet early-career engineers who have moved beyond simple chat prompts and have truly integrated AI into their development flow. Whether it’s using AI agents for autonomous debugging, analyzing logs, or rapidly prototyping and shipping features to production, we value the ability to "pilot" these tools to deliver high-quality code at an ambitious pace.
• Nice-to-have: Experience with rendering technologies like WebGL, Canvas API, or 2D graphics libraries (PixiJS, Skia)
• Nice-to-have: Real-time technologies experience (WebSocket)
• Nice-to-have: Vector databases, RAG pipelines, LangChain, or agent frameworks
• Nice-to-have: Multi-modal AI or long-context handling
• Nice-to-have: ML frameworks: PyTorch, TensorFlow, Hugging Face Transformers

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-JR1

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

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