AI Software Engineer – Level 3 or 4

Northrop Grumman Northrop Grumman · Aerospace · Dulles, VA +1 · Software

AI Software Engineer at Northrop Grumman focused on designing and implementing Reinforcement Learning (RL) and Supervised Learning (SL) algorithms for space and aerospace programs. The role involves R&D, prototyping, porting to embedded systems, developing physics-based autonomy for mission planning and decision-making, and building models for anomaly detection and response. It also includes leading verification and flight readiness campaigns and participating in the full software development lifecycle.

What you'd actually do

  1. Perform Novel Algorithm R&D
  2. Design and implement state-of-the-art RL / SL algorithms drawn from the latest literature
  3. Rapidly prototype in Python/JAX/PyTorch, then port to embedded C++/CUDA
  4. Develop Physics-Based Autonomy to perform Mission Planning & Decision-Making
  5. Apply supervised learning, reinforcement learning, and other AI/ML techniques to high-fidelity astrodynamics planning and controls problems, including real-time constraint handling

Skills

Required

  • Bachelor's degree with a minimum of 5 years of relevant AI engineering experience (or equivalent experience)
  • Master's degree with a minimum of 3 years of relevant AI engineering experience (or equivalent experience)
  • PhD with a minimum of 1 year of relevant AI engineering experience (or equivalent experience)
  • Industry knowledge and/or foundational education of AI, with a focus on ML, RL, or SL model development
  • Experience with machine learning usage in a product line environment
  • Experience with hands-on coding of learning algorithms from primary literature—comfortable translating equations to optimized code
  • Experience with physics-based AI application (e.g. for spacecraft, robotics, autonomous aircraft, drones, rockets, or similar) in academia or industry
  • Ability to obtain and maintain a U.S. Government DoD Top-Secret (TS) security clearance and Sensitive Compartmented Information (SCI) approval/access

Nice to have

  • Python/JAX/PyTorch
  • Embedded C++/CUDA
  • Astrodynamics planning and controls
  • Guidance, Navigation, and Control (GNC) filters
  • Neural search
  • Differentiable optimization
  • Anomaly detection
  • Hierarchical or policy-gradient RL
  • Monte-Carlo, Processor-in-the-Loop, Hardware-in-the-Loop, and digital twin campaigns

What the JD emphasized

  • deep knowledge of Computer Science
  • deep focus on Reinforcement Learning (RL)
  • hands-on coding of learning algorithms from primary literature
  • physics-based AI application

Other signals

  • Reinforcement Learning
  • Algorithm R&D
  • Physics-Based Autonomy
  • Spacecraft Operations