Orita

Role Description 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. Qualifications - 5+ years of full-time software engineering experience, including at least 3 years working on ML systems. - 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. - 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. - Strong proficiency in Python (numpy, pandas, etc.). - Experience with scalable data processing (Spark, Ray, BigQuery). - Job orchestration (Airflow). - Comfortable with advanced experimentation techniques. - Understanding of performance measurement in real-world deployments. - Comfortable wearing many hats—data wrangling, model development, deployment, monitoring, and performance optimization. - Excellent communication—able to explain complex ML concepts to non-technical stakeholders. - Self-starter mentality with the ability to own projects from ideation to deployment, picking up and learning new technologies as needed. Requirements - Familiarity with marketing technology or ads is a strong plus. - Experience with experimental design and methods such as causal inference or uplift modeling. - Exposure to modeling with LLMs and modern AI tooling. - Productionizing Reinforcement Learning and Bandit algorithms. - Ph.D in a technical field. - Experience in a fast-paced or startup environment. - You live in or near New York City. Most of us work in EST. Benefits - Impact: Join a lean, agile team shaping the future of ML for leading global brands. - Growth: Work directly with industry veterans with strong academic and professional backgrounds. - Innovation: Experiment with the latest ML models, from tree-based methods to cutting-edge LLMs. - Culture: We value ownership, iteration, and continuous learning—everyone’s voice matters.

Role Description We need a talented Data Engineer to help us handle massive amounts of data efficiently and reliably. You'll play a crucial role in unifying our data pipeline with an event taxonomy and normalization layer, ensuring our machine learning models have high-quality data to work with. You’ll have a huge impact on our product, our culture, and building something great from the ground up. It will be a lot of fun. As a Data Engineer, you will: - Design and build scalable and reliable data pipelines to handle large volumes of data from various sources. - Unify our data pipeline by developing an event taxonomy and normalization layer for consistent and accurate data. - Develop and maintain workflows using Airflow, dbt, and Spark. - Collaborate with data scientists and machine learning engineers to facilitate seamless data integration for model training and deployment. - Optimize data retrieval and develop data models for storage and analysis. - Ensure data quality, integrity, and security throughout the data lifecycle. - Implement ETL/ELT processes and data integration solutions. - Contribute to feature engineering efforts to enhance model performance. - Set up and analyze A/B testing in big data environments. - Monitor and troubleshoot data pipelines and workflows to maintain optimal performance. Qualifications - Proven experience as a Data Engineer, with a strong track record of delivering successful projects in data-intensive environments. - 5+ years of experience in data engineering. - Expertise in Python and SQL for data processing and manipulation. - Hands-on experience with big data tools. - Comfort with async Python. - Proficiency with DAG tools such as Airflow, dbt, or Dagster. - Willingness to work on reporting one day, and deep infrastructure the next. - Experience in building and optimizing data pipelines, architectures, and data sets. - Strong understanding of data modeling, data warehousing, and ETL/ELT development. - Familiarity with cloud platforms like AWS, GCP, or Azure, and experience deploying data solutions in the cloud. - Familiarity with dev-ops best practices, container technologies and continuous integration. - Knowledge of best practices in data governance, data quality, and data security. - Strong analytical and problem-solving skills with keen attention to detail. - Excellent communication skills, with the ability to explain complex technical concepts to non-technical stakeholders. - Strong sense of responsibility and ownership; you own projects end-to-end with a bias for solving problems and shipping impactful features into production that are well-tested. - Intellectual curiosity. You enjoy iterating and improving systems, always seeking better ways to solve complex problems. Requirements - Bonus points for experience in the following: - Background in data science or machine learning. - Experience with A/B testing frameworks in big data environments. - Knowledge of MLOps practices and tools. - Experience working in the ecommerce or marketing technology space. - Infrastructure experience, particularly with Google Cloud Platform. - Building reliable integration platforms with third-party APIs and services. Where you’ll work Remotely, with occasional in-person meetings. Bonus points if you’re based in or around New York City. Equal Opportunity Employer Orita is an Equal Opportunity Employer and does not discriminate on the basis of an individual's sex, age, race, color, creed, national origin, alienage, religion, marital status, pregnancy, sexual orientation, or affectional preference, gender identity and expression, disability, genetic trait or predisposition, carrier status, citizenship, veteran or military status and other personal characteristics protected by law. All applications will receive consideration for employment without regard to legally protected characteristics.

• Own and manage the full sales cycle for mid-market clients, from prospecting to closing • Work with our partnerships team closely. Partnerships are key to Orita, specifically our relationships with (1) Retention Agencies (2) Klaviyo CSMs and Account Executives. • Collaborate with the Co-CEO, CMO, Partnerships org and other cross-functional teams to build scalable sales processes, refine messaging, and develop collateral • Partner with the Marketing and Partnerships teams to align on lead generation strategies and create compelling outreach campaigns. • Identify customer pain points, tailor solutions, and deliver impactful presentations to executive stakeholders. • Meet revenue targets while maintaining a customer-first approach. • Realize that happy customers who stick around for the long run are the key to building a world-class business.

• Own the number. • Work as a salesperson first, to understand the opportunity and motion at Orita. You’ll partner with our Co-CEO who currently leads most deals. The first AE you’ll have is the Co-CEO. Then we’ll sprint to build the team. • Work with our partnerships team closely. Partnerships are key to Orita, specifically our relationships with (1) Retention Agencies (2) Klaviyo CSMs and Account Executives. • Note that the Orita Partnerships org *directly closes deals*. • Drive pipeline outside of the Partnerships motion too. • Partner with our Director of CS in order to ensure great customer experiences • Collaborate with the Co-CEO, CMO, Partnerships org and other cross-functional teams to build scalable sales processes, refine messaging, and develop collateral • Realize that happy customers *who stick around for the long run* are the key to building a world-class business. So, um, you will not oversell or stretch-the-truth-just-a-little as so many AEs do, in order to hit your personal goal. • The reason we are open to this title expanding beyond Head of Sales is because there is a possibility that Partnerships eventually rolls-up into this org. Today? Maybe. Down the road? Maybe. That depends on your background and skill-set, your performance, and how our organization grows over time.