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 

Contribute to 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 

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 

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