DESCRIPTION

At Northrop Grumman, our employees have incredible opportunities to work on revolutionary systems that impact people’s lives around the world today, and for generations to come. Our pioneering and inventive spirit has enabled us to be at the forefront of many technological advancements in our nation’s history - from the first flight across the Atlantic Ocean, to stealth bombers, to landing on the moon. We look for people who have bold new ideas, courage and a pioneering spirit to join forces to invent the future, and have fun along the way. Our culture thrives on intellectual curiosity, cognitive diversity and bringing your whole self to work — and we have an insatiable drive to do what others think is impossible. Our employees are not only part of history, they’re making history.
Do you want to work at an international company with endless opportunities for growth and advancement? Are you eager to join a trust-based, globally connected team, where your contributions will define what’s possible?
We are looking for team members who want to solve interesting, complex problems which protect our nation. Your talents and passion are most important to us. If you don’t know a language, program, or platform, we will teach you! Become a part of our enthusiastic team where we have fun working together and take pride in our contributions to the nation’s safety.

BASIC QUALIFICATIONS:

Sr. Principal Engineer

• Bachelor’s Degree (in Computer Science, Reinforcement Learning, or in STEM) with 8 years of experience (or 6 years of experience w/ a Masters, or 4 years w/ a PhD). Experience can be considered in lieu of degree
• Strong physics-based numerical modeling and AI/ML experience
• Industry knowledge and/or foundational education of AI, with a focus on ML, RL, or SL model development
• Proven track record of novel algorithm development (e.g., first-author papers, open-source releases, or production deployments)
• Hands-on coding of learning algorithms from primary literature—comfortable translating equations to optimized code
• Demonstrated physics-based AI application experience (e.g. for spacecraft, robotics, autonomous aircraft, drones, rockets, or similar) in academia or industry
• Proven experience in developing scalable RL/SL and other ML pipelines, with a track record of designing novel algorithms tailored to complex, real-world dynamics.
• Software Engineering Skills: Proficiency in software engineering best practices and standards, with experience in simulation development for space vehicle applications. This includes demonstrated experience in Embedded Software, Space Flight Software, or Simulation Software
• Python, CUDA, C/C++ programming experience
• Strong interest in space, national security, and related mission areas
• U.S. citizen

Staff Engineer

• Bachelor’s Degree (in Computer Science, Reinforcement Learning, or in STEM) with 12 years of experience (or 10 years of experience w/ a Masters, or 8 years w/ a PhD). Experience can be considered in lieu of degree
• Strong physics-based numerical modeling and AI/ML experience
• Industry knowledge and/or foundational education of AI, with a focus on ML, RL, or SL model development
• Proven track record of novel algorithm development (e.g., first-author papers, open-source releases, or production deployments)
• Hands-on coding of learning algorithms from primary literature—comfortable translating equations to optimized code
• Demonstrated physics-based AI application experience (e.g. for spacecraft, robotics, autonomous aircraft, drones, rockets, or similar) in academia or industry
• Proven experience in developing scalable RL/SL and other ML pipelines, with a track record of designing novel algorithms tailored to complex, real-world dynamics.
• Software Engineering Skills: Proficiency in software engineering best practices and standards, with experience in simulation development for space vehicle applications. This includes demonstrated experience in Embedded Software, Space Flight Software, or Simulation Software
• Python, CUDA, C/C++ programming experience
• Strong interest in space, national security, and related mission areas
• U.S. citizen

PREFERRED QUALIFICATIONS:

• A PhD in Computer Science with a focus on Reinforcement Learning
• Diverse programming proficiency: C/C++, Python, Matlab/Simulink, Windows/Linux scripting
• Diverse experience in modern AI/ML tools: scikit-learn, pytorch, tensorflow, ray, MLflow
• Experience in system and subsystem specification development including verification methodologies
• Knowledge of and experience with multi-agent systems and their application in achieving coordinated autonomous behavior.
• Expertise in using simulation tools (such as ROS, Gazebo, or similar) to test and validate autonomy algorithms in realistic scenarios.
• Proficiency in utilizing cloud computing platforms (e.g., AWS, Google Cloud) for scaling machine learning workloads and managing large datasets.
• Hands-on technical experience with spacecraft or satellite related systems and in validating ML methods for embedded systems
• Proven experience working with technically diverse teams across multiple locations
• Experience within Space Flight Software, Simulation Software
• Active TS/SCI clearance
  This position description does not represent a current opening but may be used to identify candidates with skills and experience for positions within Northrop Grumman that frequently become available. Candidates who express an interest may be considered for future positions at Northrop Grumman.
  The Northrop Grumman Tactical Space Division is a strategic partner specializing in commercial and classified partnerships with the design, delivery, operation and sustainment of satellites and human spacecraft. We support science and space exploration through our various partnerships, including NASA’s Artemis program with the goal to return humans to the Moon in 2024 and the TESS (Transiting Exoplanet Survey Satellite) program that has discovered more than twenty confirmed plants. Recognized as an industry leader, we also develop highly specialized space and satellite components.

Perform Novel Algorithm R&D

• Design and implement state-of-the-art RL / SL algorithms drawn from the latest literature.
• Rapidly prototype in Python/JAX/PyTorch, then port to embedded C++/CUDA.

Develop Physics-Based Autonomy to perform Mission Planning & Decision-Making

• Apply supervised learning, reinforcement learning, and other AI/ML techniques to high-fidelity astrodynamics planning and controls problems, including real-time constraint handling.
• Fuse learned policies with classical GNC filters for robust guidance, navigation, and closed-loop control.
• Build models that re-optimize delta-V, power, and comm- (among other) constrained timelines using neural search or differentiable optimization.
• Develop AI solutions for real-time anomaly detection and response to ensuring robust and adaptive spacecraft operations. This includes developing models that detect out-of-family telemetry and select corrective actions via hierarchical or policy-gradient RL
• Lead Monte-Carlo, Processor-in-the-Loop, Hardware-in-the-Loop, and digital twin campaigns to prove safety and performance per internal standards.

This position can be filled as a Level 2 or 3.