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Machine Learning Research Scientist

About Us: At Perceptronics, we are advancing technology for addressing national security challenges including electronic warfare solutions.  We harness the power of machine learning to create innovative systems that enhance operational capability.

Position Overview:  We are seeking a talented Machine Learning Research Scientist with knowledge and experience applying ML to electronic warfare problems. In this critical role, you will leverage your expertise in ML, signal processing, and electronic warfare to develop algorithms and systems focused on varied EW problems including signal detection, signal classification, anomaly detection, pulse clustering and deinterleaving, emitter identification, etc.,

We are seeking candidates who are particularly interested in using research, creativity, and problem solving to develop new algorithmic solutions to problems. Candidates must be capable of adapting and extending state-of-the-art academic research to solve critical DoD problems.

Key Responsibilities:

  • Design, develop, and optimize machine learning models for critical problems.
  • Collaborate with engineering teams to integrate machine learning solutions into hardware and software systems.
  • Stay abreast of industry trends and advancements in AI and electronic warfare and/or autonomy technologies.
  • Participate in literature reviews to identify and evaluate possible solutions to a selected problem, and creative brainstorming on adapting those solutions to the problem at hand.
  • Conduct research and experiments, presenting findings to stakeholders and translating technical concepts into actionable strategies.
  • Assist in deploying machine learning models to hardware platforms, including GPUs and FPGAs, ensuring optimized performance and efficiency.
  • Analyze and interpret complex RF signal data to enhance detection, classification, and response capabilities.

Qualifications:

  • Bachelor’s or Master’s (preferred) or PhD in Computer Science, Electrical Engineering, or a related field.
  • Proven experience in machine learning and artificial intelligence, with a focus on electronic warfare and/or autonomy applications.
  • Proficiency in relevant scientific and machine learning tools and frameworks including NumPy, Pandas, Scikit-learn, Tensorflow, Pytorch, and others.
  • Comfortable delivering high-quality, production-ready Python and C++ code that adheres to industry best practices and professional development standards.
  • Experience deploying machine learning models to hardware, including GPUs and FPGAs.
  • Strong understanding of algorithms, data structures, and statistical analysis.
  • Excellent problem-solving skills and a collaborative mindset.
  • Familiarity with key signal processing techniques, including Fourier analysis, filtering, and wavelet analysis
  • US Citizen and ability to obtain a security clearance required.

Why Join Us?

  • Be part of a mission-driven team focused on cutting-edge technology in electronic warfare.
  • Opportunities for professional development and growth in a rapidly evolving field.
  • Because Perceptronics is a member of the Pacific Defense group of companies, our growth is accelerating and there is ample room for career growth
  • Competitive salary and benefits package.

 

How to Apply: If you’re ready to tackle the exciting challenges at the intersection of machine learning and electronic warfare, we want to hear from you! Please submit your resume and a cover letter to [your email address or application link].

 

We offer competitive salaries, and great benefits including a top health insurance policy with employee’s premium 100% covered, a generous PTO policy, and flexible work schedule.

Perceptronics Solutions is an Equal Opportunity Employer and considers qualified applicants for employment without regard to race, color, creed, religion, national origin, sex, sexual orientation, gender identity or expression, age, disability, veteran status, or any other protected factor.

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EMPLOYMENT TYPE
Full-time, on-site
DATE POSTED
November 21, 2024

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