Applied Scientist

Responsibilities

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

Basic qualifications

• 4+ 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
• Publications, patents, open source contributions, or significant internal technology transfers
• Experience working with globally distributed research, product, or engineering organizations

About the company