Senior Principal Applied Scientist

Key Responsibilities

• Set the applied science roadmap for the pod across the core AI capabilities the product depends on, for example multimodal perception, computer vision, language and agentic reasoning, time series modeling, control
• Convert product and system requirements into clear research questions, hypotheses, and success metrics
• Design and own the evaluation and benchmarking frameworks for generative and predictive models, including offline metrics, online experimentation, and robustness testing in industrial conditions
• Lead applied research projects end to end, from literature review and method selection through experimentation, ablation, and productization
• Work with engineers to take models into production grade pipelines: data readiness, optimization, inference, observability
• Influence architectural and system decisions with scientific evidence and tradeoff analysis
• Identify and de-risk scaling challenges: data quality, model drift, latency, throughput, cost, safety
• Mentor scientists and engineers on experimentation rigor, reproducibility, and documentation
• Champion responsible and trustworthy AI: bias detection, model risk management, human in the loop controls

Basic Qualifications

• 10+ years in applied machine learning, AI research, or data science, with a track record of models that shipped to production and made an impact
• Strong foundation in machine learning theory and practice across training, evaluation, and deployment
• Demonstrated experience setting the science direction for a portfolio of work and shipping it through to production with engineering teams
• Proficiency in Python and modern ML frameworks and toolchains
• Track record of building evaluation and benchmarking that the team can run a roadmap against
• Clear written and verbal communication, with the ability to explain complex ML concepts to engineers, product managers, and senior leaders

Preferred Qualifications

• Experience setting science direction across multiple capability areas at the same time
• Experience bringing applied research into real world products in industrial or physical domains: manufacturing, automation, robotics, energy, mobility, infrastructure, healthcare
• Breadth across multimodal ML, generative AI, retrieval, agentic workflows, control, or planning
• Scientific ML for physical systems: surrogate modeling, operator learning, physics-informed ML, geometry-aware ML, differentiable simulation, AI for semiconductor/EDA
• Experience standing up evaluation, online experimentation, or production monitoring as practices, not just for a single model
• Publishations, patents, open source contributions, or significant internal technology transfers that the field can point to
• Experience hiring and mentoring senior or staff level scientists
• Experience working with globally distributed research, product, or engineering organizations