Senior Applied Scientist

Key Responsibilities

• Contribute to one or more scientific capability areas end to end, for example perception, computer vision, language and agents, time series, control, planning, or evaluation
• Take problems from ambiguous product or system requirements through clear research questions, hypotheses, and success metrics
• Lead applied research projects: literature review, method selection, experimentation, ablation, error analysis, and productization
• Build and run the evaluation pipelines for the work you own: offline metrics, online experiments, robustness testing in industrial conditions
• Work with engineers to take models into production grade pipelines: data readiness, training infrastructure, inference, observability
• Make scientific tradeoffs in front of engineers and product managers, with evidence, and translate them into decisions the team can act on
• Identify and de-risk scaling challenges in your area: data quality, model drift, latency, throughput, cost, safety
• Raise the bar on experimentation rigor, reproducibility, and documentation across the team
• Apply responsible AI practices in your work: bias detection, model risk management, human in the loop controls

Basic Qualifications

• 6+ years in applied machine learning, AI research, or data science, with models that shipped to production and made an impact
• Strong foundation in machine learning theory and practice across training, evaluation, and deployment
• Demonstrated experience taking research from a paper or prototype into a model that runs reliably in production
• Proficiency in Python and modern ML frameworks and toolchains
• Strong partnership track record with engineering teams on data, training infrastructure, and inference
• Clear written and verbal communication with engineers, product managers, and senior leaders

Preferred Qualifications

• Experience applying ML in industrial or physical domains: manufacturing, automation, robotics, energy, mobility, infrastructure, healthcare
• Deep expertise in one of: multimodal ML, generative AI, retrieval augmented generation, agentic workflows, time series, control, or planning
• Scientific ML for physical systems: surrogate modeling, operator learning, physics-informed ML, geometry-aware ML, differentiable simulation, AI for semiconductor/EDA
• Hands-on experience building evaluation pipelines, running online experiments, or instrumenting production monitoring for a model you owned
• Publishations, patents, open source contributions, or significant internal technology transfers
• Experience mentoring more junior scientists and engineers
• Experience working with globally distributed research, product, or engineering organizations