Current Opening

Principal Computer Scientist – Machine Learning

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Galois is a community of people dedicated to creating trustworthiness in critical systems. We are a community, not a hierarchy. We operate from citizenship, not rules. We are employee-owned. We value freedom to pursue our passions in and out of work. We operate from first principles, rather than follow the crowd.

We are hiring Principal Computer Scientists and are seeking experts interested in advancing state of the art in artificial intelligence and machine learning (including deep learning and decision making, reinforcement learning, automated planning, and anomaly detection) in ways that lead to creating trustworthy & high assurance computing systems.

As a Principal Computer Scientist you will:

  • Develop and lead an externally funded research program involving frequent client and government agency interactions.
  • Collaborate across Galois, academia, government, and industry to advance active research and development programs.
  • Publish and speak on your work, establishing a reputation for thought leadership in your domain of expertise.
  • Competitive candidates for this role will be experienced researchers in their field and have demonstrated success envisioning, designing, and leading significant research programs.

We value hands-on experience in the proposal writing process and a deep interest in pursuing programs that connect one’s research interests directly to actual customer needs. Excellent technical writing, verbal communication and public speaking skills are essential.

Requirements:



  • Ph.D. in Computer Science or a related field with a history of high quality academic or industrial research as evidenced by publications, involvement in the research community and/or patent applications.
  • A track record in obtaining external research funding.
  • The ability to adjust existing machine learning techniques in novel ways because you have a deep understanding of why the various components behave as they do (including the mathematical foundations), what function they serve in solving the general problem and how to recombine them in novel ways for a specific purpose.
  • Strong familiarity with a broad ensemble of applied machine learning techniques and the intuition/experience needed to choose the right approach to a conceptual problem, implement a prototype, and explain the tradeoffs associated with the methods chosen. This means you also have a good understanding of the math behind many of these methods.
  • Experience with many categories of machine learning tools/techniques, such as Neural Nets (including deep nets), Autoencoders, SVM, RBM, PCA, clustering algorithms, anomaly detection, Bayesian methods, generative models, etc.
  • Passionate curiosity, interest in new ideas and love of learning.

We work extensively with functional programming languages and formal methods. Our research engineers work in small teams and successfully interact with clients, partners, and other employees in a highly collaborative environment. Many of them have PhD’s in their fields as well. We’re looking for people who can invent, learn, think, inspire these teams, and our clients.

About Galois:

Galois develops technology to guarantee the trustworthiness of systems where failure is unacceptable. We apply cutting-edge computer science and mathematics to advance the state of the art in software and hardware trustworthiness. At Galois, we maintain a unique organizational structure tailored to the needs of the innovative projects we deliver. Our organizational structure is collaborative, one-level flat, and based on principles of well-defined accountabilities and authorities, transparency, and stewardship. We aspire to provide employees with something that matters to them beyond just a paycheck — whether it be opportunities to learn, career growth, a sense of community, or whatever else brings them value as a person. We believe in individual freedom in the roles we choose and in the projects we pursue — our research focus areas are the intersection of staff interests and corporate strategy. We choose practices that best suit the project, team, and leaders, with company-wide standards kept to a minimum to ensure we are making the right choices for the situation rather than just business-as-usual choices.

For more on our organizational structure, visit Life at Galois.

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