Machine Learning Automation Research Engineer

Amentum•Lancaster, CA

1d•$120,000•Onsite

About The Position

Amentum is looking to hire a full-time position at Air Force Research Laboratory, Edwards AFB, CA supporting the research and application of efficient, data-driven machine learning tools that advance our predictive and autonomous capabilities for space propulsion systems. This role includes algorithm development, uncertainty quantification, data analysis, sensor fusion, remote control of test articles, and other advanced concepts as applied to chemical and electric propulsion thrusters. Duties will range from supporting basic research in the areas of uncertainty quantification and algorithm development to applied technology development including software-in-the-loop and hardware-in-the-loop testing and analysis for in-house research and flight demonstrations.

Requirements

Must be a United States Citizen
Must be able to obtain a Security Clearance
MS or equivalent experience in computer science, machine learning, aerospace engineering, mathematics, or data science
Expertise with controls algorithms, optimization algorithms, reinforcement learning, state estimation, deep learning, or unsupervised learning
Experience working in or leading teams of scientists, engineers, and technicians

Nice To Haves

Proficiency in one or more programming languages such as Python, MATLAB, C, C++, or similar as well as software design best practices
Strong background in mathematics, statistics, or probability
Strong background with dynamical, nonlinear, or chaotic systems
Expertise in anomaly detection, causal inference, deep learning, reinforcement learning, imitation learning, optimization, state estimation, or unsupervised learning
Experience with PyTorch, Tensorflow, or similar machine learning frameworks
Experience using control principles to design feedback control systems with real-time telemetry (controls algorithms, remote systems, optimal control, uncertainty quantification, sparse measurements, noise)
Experience developing and testing code in Simulation- and Hardware-In-the-Loop (SIL/HIL) environments
Experience in sensor fusion such as Kalman filtering and experience with multiple sensing modalities
Experience with designing and integration of autonomy hardware (sensors, flight controllers)
Experience developing real-world solutions with computational constraints, measurement errors, and modeling uncertainty to create trustworthy autonomy
Must be organized, punctual, self-motivated, a team player, and a strong communicator
Highly motivated to develop space-related technologies
Strong communications skills, both written and verbal

Responsibilities

Algorithm development
Uncertainty quantification
Data analysis
Sensor fusion
Remote control of test articles
Support basic research in uncertainty quantification and algorithm development
Applied technology development including software-in-the-loop and hardware-in-the-loop testing and analysis for in-house research and flight demonstrations
Preparing written reports describing the methods, analysis, and results of experiments
Collaborate with a multidisciplinary team in an agile environment to rapidly design, prototype, and test new technologies