Job Description
The SpringCube team curated the following job opportunity to help you in your job search. Explore the position below to find your next career move.
Company Overview
A leading global creative technology company is transforming professional design through intelligent, connected experiences that bring together creativity, collaboration, and artificial intelligence. Its products empower creators and teams to work more efficiently while maintaining high standards of quality and creative expression.
The organization is seeking a Senior Machine Learning Engineer to develop and power intelligent capabilities across its products. This is a hands-on machine learning role for an experienced professional who can work across the full ML stack, from classical discriminative models for interpretation to generative models for content creation and agentic systems for intelligent action.
Key Responsibilities
- Partner with Product, Engineering, and Data Science teams to define problems, assess feasibility, and translate research into production-ready experiences.
- Design, prototype, and deploy machine learning models that power product features, including predictive models, ranking systems, generative AI, and agentic systems.
- Develop and implement modern GenAI capabilities such as Retrieval-Augmented Generation (RAG), embeddings, fine-tuning, and in-product copilots for creative use cases.
- Own machine learning evaluation from end to end by defining quality standards, developing offline and online evaluation frameworks, and identifying quality regressions in production.
- Build and maintain data and MLOps foundations that support reliable machine learning systems.
- Develop feature pipelines, experiment tracking, model versioning, CI/CD workflows, automated retraining, and production monitoring.
- Collaborate with cross-functional stakeholders to ensure machine learning solutions address meaningful product and customer needs.
Minimum Requirements
- 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 foundational knowledge of machine learning, including feature engineering and the development of supervised and unsupervised models for prediction, ranking, recommendation, or personalization.
- Practical experience with modern Generative AI technologies, including production applications of LLMs or generative models, RAG, embeddings, fine-tuning, or agent design.
- Proficiency in Python and SQL, along with familiarity with modern machine learning tools and infrastructure.
- Experience designing and operating rigorous offline and online evaluation and experimentation frameworks.
- Ability to properly frame business and technical problems before developing machine learning solutions.
- Strong communication skills with the ability to explain technical concepts clearly to Product, Design, and other non-technical stakeholders.
Preferred Requirements
- Master’s 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 a comparable production ML environment.
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