• Collaborate with cross-functional partners (Product Managers, Data Scientists, Data Engineers, Software Engineers, Business teams) to build data and Machine Learning products • Take ownership of objectives and key results for your workstream, and own technical solutions in partnership with your manager • Architect and build robust systems to train, deploy, run inference, and monitor Machine Learning and AI systems at scale • Champion code quality, reusability, scalability, maintainability, and security, and provide input into strategic architecture decisions • Implement processes and tools to ensure data quality, enforce data governance policies and engineering best practices • Integrate Machine Learning and AI systems with production applications • Innovate with new approaches, staying abreast of current research and latest technologies in the broader ML engineering community

• Develop, test, fine-tune, and optimize computer vision and object detection models using video and image datasets • Support the deployment of ML models in production or near-production environments • Analyze and process large-scale visual datasets, including annotation workflows and video sample preparation • Build new models and adapt pre-trained models to support operational and safety-focused use cases • Evaluate technical feasibility and prioritize AI/ML use cases according to business timelines and delivery goals • Collaborate with cross-functional stakeholders including Infrastructure, Operations, AI Engineering, and business teams to translate operational requirements into technical solutions • Contribute to the development of real-time detection and alerting capabilities using camera-based systems • Support rapid prototyping and MVP delivery initiatives within fast-paced environments

• Work with a cross functional team of scientists and engineers building foundational models that are used, tested, and trained on real world applications of generative design to the development of therapeutic biomolecules. • Develop and implement groundbreaking machine learning and protein science solutions. • Collaborate with computational and experimental scientists to establish these tools as a foundation of the discovery pipeline. • Work towards individual ownership and a leadership role in determining model architectures and research program needs. • Engage with open source and academic collaborations in the machine learning field.

• Build and improve the systems that power customer lifetime value modeling, from development and deployment through monitoring and production support. • Partner with data scientists to productionize statistical models, simulations, and forecasting workflows that support decision-making across the business. • Accelerate the path from research to production through scalable infrastructure, reliable workflows, and reusable tooling. • Improve the ML development experience by building better operational patterns and advancing production-ready ML practices. • Develop tools and services that help stakeholders evaluate model performance, understand business impact, and trust model outputs in production. • Collaborate with technical and business partners to solve high-value problems and improve the reliability and scalability of ML systems. • Share best practices through mentorship, documentation, and clear communication around technical decisions, tradeoffs, and operational considerations.