From Johns Hopkins Applied Physics Laboratory's posting. “We” and “our” refer to the employer.
Description
Are you passionate about machine learning, edge processing, and synthetic aperture radar imaging?
Do you seek a position to apply your engineering skills in an innovative and collaborative laboratory environment?
If so, we are looking for someone like you to join our team at the Johns Hopkins University Applied Physics Laboratory (APL)!
We are seeking an entry-level engineer or scientist to support the growth of RF/machine learning capabilities. The selected candidate will contribute in two primary areas: optimization of SAR-related processing for GPU-enabled edge hardware, and support of machine learning workflows including curated dataset development, model training, evaluation, and deployment to edge devices.
This role is intended for a candidate with strong technical fundamentals and the potential to grow into a broader RF/ML contributor through mentorship and hands-on experience. Deep SAR expertise is not required.
As a member of our team, you will…
Support development and optimization of SAR-related algorithms and processing workflows for execution on GPU-enabled edge hardware.
Assist with profiling, debugging, and improving computational performance to meet edge-device constraints such as latency, memory, throughput, and power.
Build, organize, and maintain curated datasets for machine learning training, validation, and testing.
Develop and apply data preprocessing, labeling, and quality-check workflows to prepare data for analysis and model development.
Train, evaluate, and help refine machine learning models for deployment in edge or resource-constrained environments.
Support integration and deployment of algorithms and trained models onto edge computing platforms.
Collaborate with senior staff to transition prototypes into robust, testable implementations.
Document technical approaches, results, implementation details, and performance tradeoffs.
Work closely with mentors and team members to grow technical depth in RF, SAR, machine learning, and edge deployment applications.
Contribute to the team’s emerging RF/ML capabilities through applied development, experimentation, and technical learning.
Qualifications
You meet our minimum qualifications if you have...
Bachelor’s or Master's degree in Electrical Engineering, Computer Engineering, Computer Science, Applied Mathematics, Physics, or relevant field.
Foundation in signal processing, linear algebra, and related applied mathematical methods.
Programming experience in Python, C++, or similar languages for technical computing, data processing, or algorithm development.
Familiarity with basic machine learning workflows, including data preparation, model training, evaluation, and performance assessment.
Ability to work with raw and processed data to create organized, curated datasets for analysis and model development.
Interest in performance optimization of computational pipelines, including familiarity with GPU or parallel computing concepts.
Awareness of edge or embedded computing constraints such as memory, latency, throughput, and power limitations.
Strong analytical, problem-solving, and communication skills.
Willingness to learn RF, SAR, and edge-deployed ML methods through mentorship and hands-on work.
Are able to obtain an Interim Secret Clearance by your start date and can ultimately obtain a TS/SCI. If selected, you will be subject to a government security clearance investigation and must meet the requirements for access to classified information; eligibility requirements include U.S. citizenship.
You'll go above and beyond our minimum requirements if you...
Experience with GPU programming, accelerated computing, or performance optimization tools and frameworks.
Exposure to deploying software or machine learning models on embedded or edge computing platforms.
Familiarity with machine learning frameworks such as PyTorch, TensorFlow, or similar toolkits.
Exposure to RF systems, remote sensing, image formation, SAR, or related sensing modalities.
Experience with data curation, labeling, preprocessing, or dataset management for machine learning applications.
Experience working in Linux-based development environments.
About Us
Why Work at APL?
The Johns Hopkins University Applied Physics Laboratory (APL) brings world-class expertise to our nation’s most critical defense, security, space and science challenges. While we are dedicated to solving complex challenges and pioneering new technologies, what makes us truly outstanding is our culture. We offer a vibrant, welcoming atmosphere where you can bring your authentic self to work, continue to grow, and build strong connections with inspiring teammates.
At APL, we celebrate our differences of perspectives and encourage creativity and bold, new ideas. Our employees enjoy generous benefits, including a robust education assistance program, unparalleled retirement contributions, and a healthy work/life balance. APL’s campus is located in the Baltimore-Washington metro area. Learn more about our career opportunities at https://www.jhuapl.edu/careers.
All qualified applicants will receive consideration for employment without regard to race, creed, color, religion, sex, gender identity or expression, sexual orientation, national origin, age, physical or mental disability, genetic information, veteran status, occupation, marital or familial status, political opinion, personal appearance, or any other characteristic protected by applicable law. APL is committed to providing reasonable accommodation to individuals of all abilities, including those with disabilities.
If you require a reasonable accommodation to participate in any part of the hiring process, please contact
Accessibility@jhuapl.edu.
The referenced pay range is based on JHU APL’s good faith belief at the time of posting. Actual compensation may vary based on factors such as geographic location, work experience, market conditions, education/training and skill level with consideration for internal parity. For salaried employees scheduled to work less than 40 hours per week, annual salary will be prorated based on the number of hours worked. APL may offer bonuses or other forms of compensation per internal policy and/or contractual designation.
Additional compensation may be provided in the form of a sign-on bonus, relocation benefits, locality allowance or discretionary payments for exceptional performance. APL provides eligible staff with a comprehensive benefits package including retirement plans, paid time off, medical, dental, vision, life insurance, short-term disability, long-term disability, flexible spending accounts, education assistance, and training and development. Applications are accepted on a rolling basis.
Minimum Rate
$85,000 Annually
Maximum Rate
$165,000 Annually
Responsibilities
Are you passionate about machine learning, edge processing, and synthetic aperture radar imaging?
Do you seek a position to apply your engineering skills in an innovative and collaborative laboratory environment?
If so, we are looking for someone like you to join our team at the Johns Hopkins University Applied Physics Laboratory (APL)!
We are seeking an entry-level engineer or scientist to support the growth of RF/machine learning capabilities. The selected candidate will contribute in two primary areas: optimization of SAR-related processing for GPU-enabled edge hardware, and support of machine learning workflows including curated dataset development, model training, evaluation, and deployment to edge devices.
This role is intended for a candidate with strong technical fundamentals and the potential to grow into a broader RF/ML contributor through mentorship and hands-on experience. Deep SAR expertise is not required.
As a member of our team, you will…
Support development and optimization of SAR-related algorithms and processing workflows for execution on GPU-enabled edge hardware.
Assist with profiling, debugging, and improving computational performance to meet edge-device constraints such as latency, memory, throughput, and power.
Build, organize, and maintain curated datasets for machine learning training, validation, and testing.
Develop and apply data preprocessing, labeling, and quality-check workflows to prepare data for analysis and model development.
Train, evaluate, and help refine machine learning models for deployment in edge or resource-constrained environments.
Support integration and deployment of algorithms and trained models onto edge computing platforms.
Collaborate with senior staff to transition prototypes into robust, testable implementations.
Document technical approaches, results, implementation details, and performance tradeoffs.
Work closely with mentors and team members to grow technical depth in RF, SAR, machine learning, and edge deployment applications.
Contribute to the team’s emerging RF/ML capabilities through applied development, experimentation, and technical learning.
Qualifications
You meet our minimum qualifications if you have...
Bachelor’s or Master's degree in Electrical Engineering, Computer Engineering, Computer Science, Applied Mathematics, Physics, or relevant field.
Foundation in signal processing, linear algebra, and related applied mathematical methods.
Programming experience in Python, C++, or similar languages for technical computing, data processing, or algorithm development.
Familiarity with basic machine learning workflows, including data preparation, model training, evaluation, and performance assessment.
Ability to work with raw and processed data to create organized, curated datasets for analysis and model development.
Interest in performance optimization of computational pipelines, including familiarity with GPU or parallel computing concepts.
Awareness of edge or embedded computing constraints such as memory, latency, throughput, and power limitations.
Strong analytical, problem-solving, and communication skills.
Willingness to learn RF, SAR, and edge-deployed ML methods through mentorship and hands-on work.
Are able to obtain an Interim Secret Clearance by your start date and can ultimately obtain a TS/SCI. If selected, you will be subject to a government security clearance investigation and must meet the requirements for access to classified information; eligibility requirements include U.S. citizenship.
You'll go above and beyond our minimum requirements if you...
Experience with GPU programming, accelerated computing, or performance optimization tools and frameworks.
Exposure to deploying software or machine learning models on embedded or edge computing platforms.
Familiarity with machine learning frameworks such as PyTorch, TensorFlow, or similar toolkits.
Exposure to RF systems, remote sensing, image formation, SAR, or related sensing modalities.
Experience with data curation, labeling, preprocessing, or dataset management for machine learning applications.
Experience working in Linux-based development environments.
About this listing
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