Machine Learning Engineer II developing innovative computer vision solutions for defense and commercial applications. Working on detection, classification, localization, and tracking with emphasis on algorithm integration and validation.
Responsibilities
Support development of computer vision and machine learning algorithms capable of detection, classifying, localizing, and tracking objects-of-interest from a group 1 UAV using the existing gimballed camera payload
Write and test software to support the integration of machine learning algorithms into aircraft (such as autopilots, payloads, or other functional components) or other robotic systems.
Explore and visualize data to gain an understanding of it, then identifying differences in data distribution that could affect.
Implement Machine Learning systems and validate designs through a series of purpose-designed experiments
Create objectives and develop models that help to achieve them, along with metrics to track their progress
Perform analysis tasks using AeroVironment and industry developed tools
Managing available resources such as hardware, data, and personnel so that deadlines are met
Analyze the ML algorithms to solve a given problem and ranking them by their success probability Performance when deploying the model in the real world; Verify data quality, and/or ensure it via data cleaning
Analyze the errors of the model and designing strategies to overcome them
Study and transform data science prototypes; Research and implement appropriate ML algorithms and tools
Select appropriate datasets and data representation methods; Run machine learning tests and experiments
Works on problems of moderate scope where analysis of situations or data requires a review of variety of factors. Exercises judgment within defined procedures and practices to determine appropriate action
Requirements
BS in Computer Vision and Machine Learning is required or equivalent combination of education, training, and experience
Minimum of 2 – 5 years' relevant experience
Familiarity with C/C++ and Matlab required
Proficiency with a deep learning framework such as TensorFlow or Keras
Proficiency with Python and basic libraries for machine learning such as scikit-learn and pandas
Expertise in visualizing and manipulating big datasets
Proficiency with OpenCV
Familiarity with Linux
Ability to select hardware to run an ML model with the required latency
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