Responsibilities
- Collaborate cross-functionally with specialists in multiple fields, as well as university partners, to research and develop technology that models emergent properties in social systems
- Stay current on the state-of-the-art in Machine Learning theories, practice, and software
- Collaborate with scientists in the Harmonious Communities Department to shape and craft our research program and to communicate research to Toyota collaborators and stakeholders
- Publish findings in academic journals and/or conferences (e.g., KDD, ICWSM, WWW, ACL, EMNLP, NAACL, AAMAS, CSCW)
- Contribute to technology transfer of research throughout Toyota
Qualifications
- 7+ years of experience with PhD in computer science, machine learning, computational social science, or related field, with a broad knowledge of machine learning approaches and theory. We encourage candidates to apply even if they don’t meet every qualification. We value diverse experience and are open to learning more about how your unique skills could contribute to the team.
- Experience with simulation / prefactual / counterfactual approaches (multi-agent-based modeling, GNNs, Robust Decision Modeling + AI, etc.)
- Experience with architecting ML systems across diverse data sources (text, time-series data, tabular data, etc.), including familiarity with cloud platforms (AWS, GPC, or Azure), virtualization (Docker), and orchestration (Kubeflow, Metaflow).
- Proficiency in ML frameworks (e.g. scikit-learn, PyTorch, TensorFlow, Keras), and tools for big data analysis (e.g. Databricks, Sagemaker, SQL, Spark).
- Ability to communicate complex concepts clearly across different audiences
- Strong interpersonal skills, enthusiastic collaborator, and great teammate
- Heavy interest in research to facilitate social wellbeing
Bonus Qualifications
- Experience with time series modeling and data analysis
- Ability to balance multiple projects, including short-term, targeted analyses and novel, innovative projects that may span several years
- Familiarity with human-centered research (e.g. behavioral data science, computational social science, human-computer interaction), including qualitative and/or quantitative methods such as experiments and user studies

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