• Help build our AI platform using MongoDB and Nest.js. • Coding (writing, reviewing, testing, deploying) is at the heart of what we do. • Participate in regular Scrum ceremonies and work closely with the team to integrate AI capabilities seamlessly into our application. • Help define our AI best practices and governance frameworks to ensure our AI usage is both effective and ethical. • Opportunity to shape our platform’s direction and experiment with new ideas.

• Design and build user-facing ML features that harness LLMs and generative AI to unlock new product capabilities • Partner with product, design, and ML research to prototype and deliver high-impact, ML-powered experiences • Own the technical architecture and implementation strategy for applied ML systems - balancing latency, observability, and iteration speed • Build scalable services and APIs that bring model outputs to users in trustworthy and intuitive ways • Collaborate across platform, infra, and legal/compliance teams to ensure ML deployments meet standards for safety, fairness, and performance • Establish and evangelize best practices for prompt design, model evaluation, and experimentation across the org

• On a typical day for the data science team, we sync in the morning during stand-ups to discuss the on-going work and identify blockers or areas of collaboration. • During the day, we will be brainstorming and prototyping models to solve specific business problems. • Or, we will be writing robust, well-tested code for new model deployments or supporting the design of A/B tests. • Or we can be collaborating with other teams to gain a better understanding of business domain, data or requirements

• Data mining, data cleaning, data engineering. • Select features, build, and optimize classifiers for the use of machine learning techniques. • Develop new ML algorithms to find predictive patterns. • Establish meaningful criteria for evaluating algorithm performance and suitability. • Automate model training and testing and deployment via machine learning continuous delivery pipelines. • Implement working, scalable, production-ready Machine Learning and AI Process Automation models and code. • Optimize processes for maximum speed, performance and accuracy. • Keep up to date with Machine Learning best practices and evolving open source frameworks. • Work in an agile team in a scrum process, collaborating closely with software engineers, data scientists, data engineers, subject domain experts and QA analysts.