Senior Machine Learning Engineer

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Company Overview

A leading global creative technology organization is transforming professional design through intelligent, connected tools that combine creativity, collaboration, and artificial intelligence. Its products support creators and professional teams with advanced design capabilities while incorporating emerging AI technologies to improve productivity, personalization, and creative workflows.

The organization is seeking a Senior Machine Learning Engineer to develop and power intelligent capabilities across its professional design products. This is a hands-on machine learning role focused on building solutions across the full machine learning spectrum, including classical discriminative models, generative AI, and agentic systems.

The successful candidate will work across product, engineering, and data science teams to translate research and technical concepts into production experiences. The role requires strong machine learning fundamentals combined with practical experience in modern Generative AI, model evaluation, data infrastructure, and MLOps.

Key Responsibilities

• Partner with Product, Engineering, and Data Science teams to define problems, evaluate feasibility, and translate research into production experiences.
• Design, prototype, deploy, and maintain machine learning models that power product features.
• Develop predictive, ranking, recommendation, personalization, generative, and agentic systems.
• Build and implement Generative AI solutions involving RAG, embeddings, fine-tuning, and in-product AI copilots for creative applications.
• Develop models and systems that support intelligent and personalized creative experiences.
• Own model evaluation from end to end by defining quality standards and developing offline and online evaluation frameworks.
• Monitor production systems and identify quality regressions and opportunities for model improvement.
• Build reliable data and MLOps foundations, including feature pipelines, experiment tracking, model versioning, CI/CD, automated retraining, and monitoring.
• Collaborate with cross-functional stakeholders to ensure machine learning solutions effectively address product and user needs.
• Communicate technical concepts and machine learning outcomes clearly to Product, Design, and other non-technical stakeholders.

Required Qualifications

• Bachelor’s degree in a quantitative field such as Computer Science, Machine Learning, Data Science, Engineering, or a related discipline, or equivalent practical experience.
• 5+ years of experience building, deploying, and operating machine learning systems in production at scale.
• Strong foundation in machine learning, including feature engineering and supervised and unsupervised modeling.
• Experience developing and deploying models for prediction, ranking, recommendation, or personalization.
• Practical experience with modern Generative AI technologies, including LLMs or other generative models in production environments.
• Experience with RAG, embeddings, fine-tuning, or agent design.
• Proficiency in Python and SQL.
• Familiarity with modern machine learning tooling, infrastructure, and production ecosystems.
• Experience designing and operating rigorous offline and online evaluation and experimentation processes.
• Ability to properly frame business and product problems before selecting and developing machine learning solutions.
• Strong communication skills with the ability to explain technical concepts to Product and Design stakeholders.

Preferred Qualifications

• Master’s degree or PhD in a quantitative discipline.
• Experience with consumer product analytics, particularly within creative tools, SaaS, or subscription-based businesses.
• Experience developing recommendation systems or personalization solutions, particularly for visual or creative content.
• Experience deploying and monitoring machine learning models using Databricks, including MLflow, Feature Store, and model serving, or comparable production machine learning environments.
• Experience working with large-scale AI and machine learning platforms.
• Experience taking machine learning models from experimentation through production deployment and continuous monitoring.

Disclaimer

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