Senior Software Engineer, Shield AI

• Typically requires a minimum of 5 years of related experience with a Bachelor’s degree; or 4 years and a Master’s degree; or 2 years with a PhD; or equivalent work experience
• Prioficiency 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

Desirable

• 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 develop and deploy advanced machine learning models that solve real-world perception challenges for autonomous systems
• You'll own major features from model development through deployment, working closely with machine learning researchers, perception engineers, autonomy engineers, and platform teams to bring cutting-edge AI capabilities into production
• This is an ideal opportunity for engineers who enjoy solving difficult perception problems while building reliable, production-ready ML systems that operate on autonomous platforms in complex operational environments
• 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