Senior Staff Machine Learning Engineer
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 air traffic and surveillance sensor systems, Saab is incorporating these advancements into both existing systems, as well as systems that are in the conceptual stage. To take advantage of these opportunities, Saab is seeking a Senior Staff Artificial Intelligence (AI)/Machine Learning (ML) Engineer. Responsibilities will include:
Work with other members of the Strategic Portfolio Office (SPO) and product engineering to develop enhancements to existing products using AI/ML as well as develop new applications targeting future efforts.
Study, apply, and evolve AI/ML capabilities developed in other parts of Saab and available off the shelf (commercial and open source).
Help test and evaluate suitable technologies and approaches.
Design and develop AI/ML algorithms and architectures to support a variety of use cases, such as:
Improved discrimination performance in RF systems.
Develop AI/ML algorithms to achieve capabilities against agile, adaptive, and unknown hostile radars or radar modes.
AI-enabled computer vision capabilities to support airport operations.
Large-scale surveillance and flight information analytics for historical analyses and real-time predictions.
While this role is based in East Syracuse NY, this position can be filled by a highly qualified candidate looking for a challenging and rewarding assignment while working from their preferred remote location.
Skills and Experience:
Required Education and Experience:
Bachelor's degree in Computer Engineering, Computer Science, Mathematics, Machine Learning or related discipline is required. 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.
Understanding of real-time radar processing (signal processing, radar scheduling, data processing, tracking).
Understanding of air traffic management systems.
Strong, demonstrated machine learning/artificial intelligence 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.
Must be a U.S. citizen. Applicants selected may be subject to a government security investigation and must meet eligibility requirements for access to classified information. As a condition of employment, candidates will be required to participate in a background check that will include, at a minimum, a criminal record check, education and employment verification.
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 17,000 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, disability status, or any other federal, state, or locally protected category.