Miro

Account Executive

• Combine the interests of multiple client stakeholders to drive account revenue growth.
• Enterprise account planning to motivate and lead company resources in appropriate expansion strategies.
• Break into new personas and lines of business to drive incremental use cases.
• Ensure we have accurate data in our CRM to drive the strategy of future growth.
• Achieve monthly, quarterly and annual quotas of pipeline and close business.

Enterprise Account Executive – Spanish-speaking

• Drive Revenue Growth: Close high-value deals with strong business cases and measurable customer outcomes
• Pipeline Generation: Dedicate 8-10 hours weekly to outbound prospecting across both new logos and expansion accounts
• Executive Engagement: Build relationships with VP+ decision-makers while creating Director+ champions to drive deals forward
• Sales & Qualification Methodology Execution: Leverage MEDDPICC and Command of the Message to qualify and advance opportunities
• Customer-Centric Selling: Develop a tailored value hypothesis for each of your accounts and develop tailored solutions that help the customer realize this value
• Cross-Functional Collaboration: Work closely with Customer Success, Solutions Engineering, Value Advisory, Professional Services, Field Marketing, and Sales Development to orchestrate winning account strategies
• Adaptability & Grit: Demonstrate resilience, humility, and openness to change in a fast-paced, evolving environment

Strategic Account Executive

AI Engineer

• 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

Procurement

• Develop and execute comprehensive sourcing strategies for IT, Marketing, Travel, and Professional Services to maximize value and minimize risk.
• Lead the full RFP process, from identifying business requirements and market research to final contract execution.
• Act as a trusted advisor to internal stakeholders, helping them find the best solutions while ensuring alignment with Miro’s financial and operational goals.
• Negotiate complex commercial terms and Service Level Agreements (SLAs) to ensure high-quality delivery and cost-efficiency.
• Collaborate with the wider S2P team to refine our processes, making sourcing at Miro more seamless and data-driven for everyone.
• Act as the primary liaison between our business units and internal experts.

Strategic Sourcing Category Manager

Research Scientist

• Design and train custom architectures that fuse multimodal inputs
• Read the latest research papers and prototype solutions
• Define the technical strategy on model selection
• Conduct rigorous code reviews and mentor research engineers
• Solve ambiguous problems with no existing solutions

Lead Research Scientist

Research Engineer

• 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 applications.
• 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.

Machine Learning Research Engineer

Machine Learning Engineer

• 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.