
Calling All TS Early Career AI Algorithm Development Engineers (Pipeline) (Dulles Space Park COS)
- Virginia
- $108,200-162,200 per year
- Permanent
- Full-time
- 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.
- 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.
- Bachelor's Degree (in Computer Science, Reinforcement Learning, or in STEM) with 2 years of experience (or 1 year of experience [outside of internships/graduate research/etc.] w/ a Masters, or 1 year [outside of internships/graduate research/etc.] 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 with active Top Secret clearance at time of application
- Bachelor's Degree (in Computer Science, Reinforcement Learning, or in STEM) with 5 years of experience (or 3 years of experience w/ a Masters, or 1 year [outside of internships/graduate research/etc.] 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 with active Top Secret clearance at time of application
- 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