Shield AI is a venture-backed defense-tech company with the mission of protecting service members and civilians with intelligent systems. Its products include Hivemind autonomy software, V-BAT and X-BAT aircraft, and Aechelon simulation and synthetic reality technologies. With offices and facilities across the U.S., Europe, the Middle East, and Asia-Pacific, Shield AI’s technology actively supports operations worldwide. For more information, visit www.shield.ai. Follow Shield AI on LinkedIn, X, Instagram, and YouTube.
Job Description:
Bring promising AI research into autonomous systems that operate in the real world. Shield AI’s Perception team is looking for an engineer who enjoys exploring new ideas and making them work on real hardware. You’ll help turn advances in multimodal computer vision, visual foundation models, and embodied AI into useful capabilities for autonomous aircraft and vehicles.
Working alongside researchers and autonomy engineers, you’ll evaluate promising approaches, build prototypes, and carry successful ideas into the Hivemind product. You’ll combine learned methods with established perception techniques, working through the sensor, software, and compute challenges that make edge robotics different from a benchmark.
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Full-time regular employee offer package:
Pay within range listed + Bonus + Benefits + Equity
Temporary employee offer package:
Pay within range listed above + temporary benefits package (applicable after 60 days of employment)
Salary compensation is influenced by a wide array of factors including but not limited to skill set, level of experience, licenses and certifications, and specific work location. All offers are contingent on a cleared background and possible reference check. Military fellows and part-time employees are not eligible for benefits. Please speak to your talent acquisition representative for more information.
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Shield AI is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, marital status, disability, gender identity or Veteran status. If you have a disability or special need that requires accommodation, please let us know.
What you'll do:
Evaluate emerging models and methods against representative sensor data and autonomy needs.
Build prototypes and experiments that reveal strengths, limitations, and practical deployment opportunities.
Combine learned and classical approaches to improve detection, tracking, geometric vision, or sensor fusion.
Optimize and integrate capabilities for edge deployment, contributing Python and C++ software with support from experienced product engineers.
Help deliver tested, documented capabilities and stay involved as other teams adopt them.
Required qualifications:
A PhD in computer vision, machine learning (ML/DL) , robotics, or a related field, or an equivalent record of advanced research and hands-on engineering.
Research depth in an area such as multimodal vision, visual foundation models, video understanding, open-vocabulary perception, or embodied learning.
Strong Python skills and experience with PyTorch or a comparable ML framework.
A thoughtful approach to experiments, evaluation data, and understanding why models fail.
Experience building substantial research software, an interest in developing production-quality C++, and a collaborative approach to solving problems.
Preferred qualifications:
Experience bringing a research prototype into sustained use.
Experience with robotics, real sensors, temporal data, or multi-sensor perception .
Familiarity with vision-language models, vision-language-action models, or embodied AI.
Experience with C++, edge inference, or model optimization using tools such as ONNX, TensorRT , or CUDA.
You don’t need to meet every preferred qualification or arrive as an expert C++ engineer. We welcome recent PhD graduates and early-career researchers, and we value technical depth, curiosity, and enthusiasm to transfer research to business value more than publication count alone.