- 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
Benefits
- Excellent Medical Coverage
- Mental Health Employee Assistance Program
- Paid Parental Leave
- Pet Insurance
- Flexible Work Hours
- Onsite Gym (DC)
- Gym Discount (San Diego)
- Free Parking
- Competitive Compensation
- Stock Benefits
- 401K Services and Match
Experience training an deploying ML models for computer vision in a production settingPrioficiency of machine learning fundamentalsExperience with machine learning frameworks such as PyTorch and TensorFlowDemonstrated expertise in deploying models using TensorRT and ONNXProficiency in C++ and PythonStrong analytical and problem-solving skills, with the ability to translate research into practical applicationsStrong understanding of 3D vision problems/algorithmsTypically 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 experienceAbility to obtain a SECRET clearanceExperience with developing autonomous systems for defense customersExperience with training/finetuning vision-language models, vision-language-action models, and/or world modelsContributions to open-source projects in machine learning or computer visionTrack record of publications in leading computer vision and robotics conferences and journals (e.g., CVPR, ICCV/ECCV, RAL, ICRA)
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