Machine Learning Engineer; Saab Defense and Security

Vacancies 07 March 2019

At Saab, we constantly look ahead and push boundaries for what is considered technically possible. We collaborate with colleagues around the world who all share our challenge – to make the world a safer place.

Closing date

09-Aug-2019

Location

Syracuse, NY

Contact

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What you will be part of

At Saab Sensor Systems you will have the opportunity to collaborate with world-class engineers and professionals in an environment that fosters career development, collaboration and support at all levels.  A business unit of Saab Defense and Security USA LLC, Sensor Systems is focused on the design, development, integration, test, deployment, and whole life support of surveillance sensor systems, primarily for industry partners and US Government agencies.  Headquartered in Syracuse, NY, we also offer opportunities to work from locations in Florida, North Carolina and Virginia.

Your role

Advancements in fields such as artificial intelligence and machine learning have the capacity to revolutionize everything from how combat decisions are made to when ship maintenance may be needed. As a developer of surveillance sensor systems, Saab is in a position to incorporate these advancements into both existing systems as well as systems that are in the conceptual stage. To take advantage of these opportunities, Saab is in need of a Machine Learning Engineer.

In this role you will:

  • Develop foundations for applying machine learning to the RF spectrum domain.
  • Develop practical applications to improve discrimination performance in RF systems.
  • Develop machine learning algorithms to achieve capabilities against agile, adaptive, and unknown hostile radars or radar modes.

Your experiences and skills

Required Education and Experience:

  • Bachelor’s degree in computer engineering, computer science, mathematics, or machine learning. Master's degree or Ph.D. is preferred.
  • Direct software development experience with in C++, Python, Java, MATLAB, and/or R.
  • Experience with frameworks for Neural Networks and Machine Learning models (TensorFlow, scikit-learn, Keras, Spark MLlib, etc.)
  • Strong background in mathematics and/or statistics knowledge and analytical problem solving skills.

Desired Skills:

  • Understanding of real-time radar processing (signal processing, radar scheduling, data processing, tracking).
  • Strong, demonstrated machine learning background, with hands-on experience building real systems.
  • Understanding of state-of-the-art machine learning and deep learning techniques and best practices.
  • Excellent written and verbal communication skills; comfortable presenting research to large audiences.
  • Comfortable communicating with diverse groups (both experts and novices) in technical and non-technical roles.

As a condition of employment, candidates will be required to participate in a background investigation and/or successfully demonstrate eligibility to receive applicable Government security clearance(s).

As a contractor for the United States Department of Defense, Saab Defense and Security USA LLC is an E-Verify participant and applicants must be U.S. Citizens in order to be considered for employment. 

Saab is a global defense and security company operating in the fields of air, land and naval defense, civil security and commercial aeronautics. We number approximately 15,500 employees and have operations on all continents. Technologically we are leaders in many areas, and one-fifth of our earnings are spent on research and development.

Saab is a company where we see diversity as an asset and offer unlimited opportunities for advancing in your career. We are also a company that respects each person’s needs and encourage employees to lead a balanced, rewarding life beyond work. Saab values diversity and is an Equal Opportunity/Affirmative Action employer. All qualified individuals are encouraged to apply and will be considered for employment without regard to race, color, religion, national origin, sex (including pregnancy), sexual orientation, gender identity, age, veteran, or disability status.

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