Shield AIでは、自律システム向けの次世代認識技術を開発するStaff Software Engineerを募集しています。機械学習およびコンピュータビジョンの専門知識を活かし、研究を実際の製品へと実装する役割を担います。C++とPythonの習熟に加え、PyTorchやTensorRTの活用経験が必須です。3Dビジョンアルゴリズムへの深い理解と、本番環境でのモデルデプロイ経験が求められます。国防関連の自律システム開発経験者は優遇されます。
Washington, DC / Dallas, Texas / San Diego, California / Boston, MA Hivemind Solutions Division – Flight System Integration / Full Time Employee / On-site
Founded in 2015, 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 and V-BAT and X-BAT aircraft. 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.
The Hivemind Solutions Perception team develops the next generation of perception capabilities for autonomous systems by combining state-of-the-art machine learning with the proven foundations of computer vision. The team advances how autonomous platforms understand and interpret the world by developing vision, vision-language (VLM), and vision-language-action (VLA) models that tackle core perception challenges such as object understanding, scene interpretation, and mission-relevant environmental awareness. Working at the intersection of research and production, our engineers build the data pipelines, supervised fine-tuning (SFT) workflows, evaluation frameworks, and deployment infrastructure needed to transform cutting-edge AI research into reliable, mission-ready perception capabilities.
In this role, you'll lead the technical development of advanced machine learning solutions that define the future of perception for autonomous systems. You'll own the team's most challenging technical problems, drive architecture and model development across multiple efforts, and influence how foundation models are adapted, evaluated, and deployed for real-world autonomy. Working closely with researchers, perception engineers, autonomy engineers, and platform teams, you'll bridge cutting-edge AI research with scalable production systems while mentoring engineers and raising the technical bar across the organization.
Model Development – Design, train, fine-tune, and maintain state-of-the-art vision, vision-language, and vision-language-action models that improve perception and decision-making for autonomous systems.
Data Pipelines & Model Training – Build scalable data pipelines, supervised fine-tuning (SFT) workflows, and evaluation loops that continuously improve model performance on mission-relevant tasks.
Model Deployment & Optimization – Deploy and optimize machine learning models for embedded hardware using technologies such as ONNX, TensorRT, and hardware-accelerated inference frameworks.
Perception & Autonomy Applications – Apply modern machine learning techniques to solve challenging perception and autonomy problems across aerial and other autonomous systems operating in complex, real-world environments.
Research-to-Production – Translate cutting-edge machine learning research into production-ready capabilities by balancing model performance, robustness, computational efficiency, and operational reliability.
Cross-functional Collaboration – Partner closely with perception, autonomy, platform, and software engineering teams to integrate machine learning capabilities into mission-ready autonomous systems.
Model Evaluation & Validation – Develop benchmarks, testing methodologies, and evaluation frameworks to measure model performance, identify failure modes, and guide future improvements.
Continuous Improvement – Improve training infrastructure, developer tooling, deployment workflows, and model lifecycle management to accelerate experimentation and production delivery.
Typically requires a minimum of 7 years of related experience with a Bachelor’s degree; or 6 years and a Master’s degree; or 4 years with a PhD; or equivalent work experience.
Expertise of machine learning fundamentals.
Experience training an deploying ML models for computer vision in a production setting.
Strong understanding of 3D vision problems/algorithms.
Experience with machine learning frameworks such as PyTorch and TensorFlow.
Demonstrated expertise in deploying models using TensorRT and ONNX.
Proficiency in C++ and Python.
Strong analytical and problem-solving skills, with the ability to translate research into practical applications.
Ability to obtain a SECRET clearance
Experience with developing autonomous systems for defense customers.
Experience with training/finetuning vision-language models, vision-language-action models, and/or world models.
Contributions to open-source projects in machine learning or computer vision.
Track record of publications in leading computer vision and robotics conferences and journals (e.g., CVPR, ICCV/ECCV, RAL, ICRA).
$195,200 - $292,800 a year
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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.
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.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
