Senior Machine Learning Engineer

About the role

Movable Ink scales content personalization for marketers through data-activated content generation and AI decisioning. The world’s most innovative brands rely on Movable Ink to maximize revenue, simplify workflow and boost marketing agility. Headquartered in New York City with close to 600 employees, Movable Ink serves its global client base with operations throughout North America, Central America, Europe, Australia, and Japan.

Join a team where AI isn’t a buzzword—it’s the engine of our success. We’re looking for a technical leader to bridge the gap between research and reality, productizing sophisticated models that scale. At Movable Ink, you’ll advance a world-class ML Platform in a fast-paced environment where every release makes a global impact. If you’re ready to shape the intersection of digital marketing and artificial intelligence, this is your next challenge.

Responsibilities:

- Deploy robust, commercial-grade AI products that redefine client expectations.

- Seamlessly integrate breakthrough innovations into our core AI models suite.

- Engineer high-performance, scalable ML systems that maintain impeccable reliability under pressure.

- Elevate our ML Platform with industry-leading standards for monitoring, orchestration, and automated testing.

- Drive product adoption and client satisfaction by delivering sophisticated AI-driven insights.

- Ship high-impact AI models that translate directly into tangible business growth.

Qualifications:

- 5+ years of experience in AI/ML development, with a focus on architecting deep learning systems and sophisticated model pipelines

- Master’s degree or higher in Computer Science, Mathematics, Engineering, or related technical field

- Proven track record of productizing breakthrough AI research into commercial-grade deep learning models

- Deep expertise in neural network architectures, including transformer architecture, computer vision, natural language processing, and multimodal learning

- Experience in model optimization and fine-tuning for high-performance inference at scale

- Advanced proficiency in PyTorch or TensorFlow, with the ability to implement and adapt state-of-the-art research papers

- Proficiency in Python along with experience with machine learning libraries and frameworks (e.g. scikit-learn, XGBoost,...)

- Experience with large-scale data processing frameworks (we use Spark) and cloud computing platforms (we use Google Cloud Platform) is desirable

- Experience designing and maintaining production ML systems at scale requiring complex workflow orchestration, artifact versioning, online/offline evaluation, performance monitoring, and A/B testing

- Strong background in statistical modeling and predictive analytics for recommender systems and propensity scoring

- Strategic understanding of the AI model lifecycle, from discovery and experimentation to production deployment and evaluation