Senior Machine Learning Engineer

## About the company Orita builds AI customer segments for many of the best brands in the world including (deep breath) Spanx, ThirdLove, True Classic, Tracksmith, Harney & Sons, Sun Bum, Ministry of Supply, Thursday Boots, gorjana, and hundreds more. Orita's algorithms help brands understand who wants to hear from them, when, and through what channel (email, SMS, direct mail today, more coming soon …). By messaging prospects and customers when they're actually listening, you're able to make a bunch of money. In a world where acquisition costs are skyrocketing, fixing retention and driving LTV is the key to profitable growth. ## The Role As a Senior Machine Learning Engineer at Orita, you will: - Build and Productionize Models: Design, train, and deploy models that directly power our marketing-focused products, primarily for marketing use cases. - Develop Scalable ML Infrastructure: Architect and maintain robust, scalable, MLOps pipelines to ensure reliable training, serving, and monitoring of models in production. - Experiment & Optimize: Drive continuous improvement using A/B testing, uplift modeling, causal inference, and other advanced experimentation frameworks to validate and refine model performance. - Collaborate & Mentor: Work closely with cross-functional teams, including the CEO and CTO, to align on product goals and foster best practices for machine learning and data engineering across the organization. ## Ideal Background Please apply even if you don't meet every requirement. We're looking for a versatile engineer who can learn quickly and own problems end-to-end. ## Education & Experience - 5+ years of full-time software engineering experience, including at least 3 years working on ML systems. ## ML Expertise - Deep knowledge of modern machine learning algorithms (tree-based methods, deep learning architectures, transformers/LLMs). - Hands-on experience with PyTorch, TensorFlow, XGBoost or equivalent frameworks. - Feature engineering using aggregations, embeddings, and sub-models. ## MLOps & Cloud - Track record building production-scale ML infrastructures, ideally using GCP (Vertex AI, KubeFlow, BigQuery, etc.). - Familiarity with CI/CD, containerization (Docker/Kubernetes), and distributed training (Spark, Ray, Dask, etc.). - Experience iterating models in a production environment is a must. ## Software Engineering Skills - Strong proficiency in Python (numpy, pandas, etc.). - Experience with scalable data processing (Spark, Ray, BigQuery). - Job orchestration (Airflow) ## Analytical & Statistical Background - Comfortable with advanced experimentation techniques. - Understanding of performance measurement in real-world deployments. ## Soft Skills & Culture - Comfortable wearing many hats—data wrangling, model development, deployment, monitoring, and performance optimization. We value ownership of the full lifecycle. - Excellent communication—able to explain complex ML concepts to non-technical stakeholders. - Self-starter mentality with the ability to own projects from ideation