Calling all AI Algorithm Development Engineers

Bachelor’s Degree (STEM field preferred) with 14 years of experience (or 12 years of experience w/ a Masters, or 9 year w/ a PhD). Experience can be considered in lieu of degree
A Master’s in Computer Science, Reinforcement Learning, or a STEM field preferred with a 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 machine learning usage in a product line environment
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. citizenship

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 Verification & Flight Readiness
Lead Monte-Carlo, Processor-in-the-Loop, Hardware-in-the-Loop, and digital twin campaigns to prove safety and performance per internal standards
Participate in the full life-cycle of software development, to include requirements development, modeling and design, application development, unit to CSCI testing, integration, formal system testing, release, installation, and maintenance

A PhD in Computer Science with a focus on Reinforcement Learning
Diverse programming proficiency: C/C++, Python, Matlab/Simulink, Windows/Linux scripting
Demonstrated systems engineering excellence in requirements analysis, modeling and simulation, verification and validation at subsystem and system level or Demonstrated vehicle development, mission systems integration, ground and flight test validation experience
Experience in successfully developing and integrating software and hardware solutions for space applications
Experience in system and subsystem specification development including verification methodologies
Experience implementing action plans based on metrics and/or root cause/corrective actions
Hands-on technical experience with spacecraft or satellite related systems
Proven experience working with technically diverse teams across multiple locations
Experience within Space Flight Software, Simulation Software
Active TS/SCI clearance