Machine Learning Engineer/Researcher - Multiple Locations

Galois Arlington , VA 22204

Posted 3 weeks ago

About Galois:

Galois tackles the hardest problems in computer science. Our mission is to assure trust in critical systems that protect the privacy and integrity of information in the real world. Core to this is the application of formal analysis techniques that allow systems to be modeled, analyzed, and mathematically proven correct to ensure that a system behaves exactly as we intend under all circumstances.

We are a community of researchers, engineers, and operations people who are dedicated to creating trustworthiness in critical systems in every functional area of the organization. Our organization is highly collaborative, and we pursue working from first principles, transparency, building deep trust, learning and innovating, and creating space for you to be authentically you. Our unique organizational structure enables us to adapt to the needs of the innovative projects we deliver.

We are employee-owned and aspire to provide employees with a sense of freedom to pursue passions in and out of work whether it be opportunities to learn, career growth, a sense of community, or whatever else brings you value as a person.

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

About This Role:

Galois is hiring a Senior Machine Learning Research Engineer to join our team! The role will primarily focus on the application of trustworthy artificial intelligence, machine learning, data science, and modeling important, often real-time problems. Much of this work incorporates human-machine teaming and the augmentation of domain experts with machine intelligence to improve crisis response, real-time decision making, and to enable trustworthy and high confidence decision-making. Engineers work in small team settings and must successfully interact with clients, partners, and other employees in a highly cooperative, collaborative, and intellectually challenging environment.

Key Qualifications:


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  • Education: Minimum of a Masters degree in Computer Science Applied Mathematics, Statistics or related field. PhD is desirable but optional.
  • Required Technical Expertise:
    • Must have hands-on experience developing software and/or performing CS research.
    • Must have demonstrated experience working with, and evaluating, ML models derived from large data sets.
    • Must have experience with one or more machine learning software packages (e.g., PyTorch, TensorFlow, scikit-learn, Caffe, Theano).
    • Must have experience with SQL or NoSQL databases, ideally within the scope of machine learning applications.
  • Desired Technical Expertise:
    • Experience with scientific thinking on long and short-term research projects.
    • Background in mathematical optimization and/or signal processing techniques.
    • Fluency in one or more programming languages.
  • Required General Skills:
    • Must work well with customers, including building rapport, identifying needs, and communicating with strong written, verbal, and presentation skills. Must be highly motivated and able to self-manage deadlines and quality goals.
    • The candidate will be subject to a security investigation and will need to meet eligibility requirements for access to classified information.
  • Location:

    We enjoy a hybrid work environment, and being co-located with one of our offices is preferred locations in Arlington, VA, Dayton, OH, Minneapolis, MN, and Portland, OR. Potential for remote work is possible.

    Equal Employment Opportunity:

    Galois is an Equal Opportunity Employer and does not discriminate in employment opportunities or practices based on race, ethnicity, national origin, ancestry, color, sex, gender identity or expression, sexual orientation, marital or parental status, pregnancy or childbirth, disability, age, religion, creed, genetic information, veteran status, or any other characteristic protected by applicable federal, state, or local law. We encourage and respect different viewpoints and experiences as being essential to the process of innovation. We strive to acquire, grow, and maintain a diverse and inclusive workplace that applies principles and standards equitably while supporting the needs and accommodations of the individual employee.

    Consistent with the Americans with Disabilities Act (ADA) and federal and state laws, it is the policy of Galois, Inc. to provide reasonable accommodation when requested by a qualified applicant or employee with a disability, unless such accommodation would cause an undue hardship. If you require reasonable accommodation in completing the employment application, interviewing, completing any pre-employment testing, or otherwise participating in the employee selection process, please contact peopleoperations@galois.com.

    The position may include access to technology and/or software source code that is subject to U.S. export control requirements under the ITAR or the EAR.

    Benefits

    We offer a robust benefits package to provide for your and your familys well-being, including:

    • Employee Stock Ownership Plan (ESOP)
    • 401(k) retirement plan with 5% employer match and immediate vesting
    • Fully paid medical insurance plans and dental and vision reimbursement plan
    • Health Savings Account (HSA) with generous employer contributions
    • Mental health and wellbeing support through our employee assistance program
    • 5 weeks of paid time off and 9 days of paid company holidays each year
    • 16 weeks of fully paid parental leave (available for new parents for birth, adoption, and fostering)
    • 1 week of fully paid Blue Sky innovation time each year to pursue your interests

    For more information on our benefits, visit Careers at Galois.

    Compensation:

    Compensation is based on the value of your results, not your value as an employee or person. The compensation process, individual salaries, and criteria for salary changes are transparent to the entire company.

    For more information about our forward-looking and transparent approach to pay, visit Compensation.

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