Senior Integrated Symbolic Artificial Intelligence (AI) and Machine Learning (ML) Scientist

Summary:

Are you passionate about combining symbolic artificial intelligence (AI) and sub-symbolic/machine learning approaches to leap ahead of current limitations in artificial intelligence (AI) architectures and systems? We are seeking a senior-level scientist with demonstrated expertise in symbolic AI methods such as rule-based systems; AI planning and search such as Monte-Carlo tree search (MCTS), hierarchical task networks (HTNs), and adversarial search; knowledge representation; symbolic inference and ontological reasoning; and constraint reasoning as well as sub-symbolic AI systems such as deep learning, probabilistic programming languages, and probabilistic program synthesis. We are working to create AI systems that fundamentally combine these technologies for both learning from data and perform advanced reasoning over the learned representations. Example ongoing projects include:

  • Creating models for explaining the causal reasoning of deep learning systems
  • Combining machine learning, probabilistic program synthesis, and AI-based planning/reasoning learning new knowledge representations for changes in the world and then reasoning within these newly created representations
  • Using deep-learning-based inference compilation to invert cognitive models to understand human behavior and help search and rescue teams perform their tasks

In this position, you will collaborate with and lead teams of AI scientists, software engineers, and domain subject matter experts. You will advance the state of the art in combining these symbolic methods to sub-symbolic, statistical AI approaches such as machine learning, including deep learning for classification and reinforcement learning; probabilistic programming and reasoning with uncertainty; probabilistic program synthesis; inference compilation; and automatic knowledge representation refinement and creation.

You will apply this research to foundational basic research and high-impact immediate, real-world challenges as you follow your interests. You will have the freedom to identify and pursue your own research directions, and collaborate with others both down the hall and across the research community as you lead projects, collaborate on projects and establish new research areas within these fields.

Recent related Charles River Analytics news and efforts:

Keynote on the future of probabilistic programming Dr. Avi Pfeffer, Charles River Analytics’ Chief Scientist (https://www.cra.com/company/news/latest-probabilistic-programmingbr-charles-river-analytics-dr-avi-pfeffer-delivers)

Probabilistic extensions for Systems Modeling Language in ProbSysML (https://www.cra.com/work/case-studies/probsysml)

Explainable deep learning in CAMEL (https://www.cra.com/work/case-studies/camel)

Areas to be Explored:

Symbolic AI methods

  • Rule-based systems
  • Planning and search systems
    • Monte-Carlo Tree Search (MCTS)
    • Hierarchical Task Networks
    • Adversarial Search
    • Heuristic-based Search and Planning Methods
  • Knowledge representation
  • Constraint Reasoning

Sub-symbolic, statistical AI methods

  • Machine Learning and Inference
    • Deep Learning
    • Classification
    • Reinforcement Learning
  • Probabilistic Programming and Reasoning
  • Probabilistic Program Synthesis
  • Inference Compilation
  • Automatic Knowledge Representation refinement and creation

Major Responsibilities/Activities:

  • Analyze and understand customer problems and issues to convert these insights into system requirements
  • Pursue novel technologies to implement solutions, including establishing new areas of research
  • Present innovative technical solutions at briefings, workshops, and conferences to customers, collaborators, and the research community
  • Work with experienced software engineers to develop and implement solutions
  • Conduct technical discussions with customers
  • Write and contribute to proposals, reports, and research papers

Minimum Requirements:

  • U.S. Citizenship
  • Doctorate degree in Computer Science (or a related field) with a focus on symbolic and sub-symbolic AI or a strong track record conducting research in these areas
  • Strong verbal and written skills to support proposal writing, interaction with customers, and presentations at technical conferences
  • Significant experience with symbolic AI in one or more sub-fields and some experience sub-symbolic AI

Benefits:

Charles River Analytics offers competitive compensation plus bonus and profit-sharing, with an attractive benefits package including: 100% employer-paid medical and dental insurance, as well as vision, life and disability insurance, paid maternity/paternity leave, tuition reimbursement, monthly gym allowance, free parking, generous paid time off, and a casual environment. We are also accessible by public transportation.

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