Develop and deploy advanced vision, VLM, and VLA machine learning models for autonomous systems perception. Own models from training through optimization and deployment on embedded hardware, collaborating with research and engineering teams to deliver production capabilities for complex real-world environments.
163k – 245k/yr
On-site5+ YOEML Engineering
About the role
What You'll Do
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.
Build scalable data pipelines, supervised fine-tuning (SFT) workflows, and evaluation loops that continuously improve model performance on mission-relevant tasks.
Deploy and optimize machine learning models for embedded hardware using technologies such as ONNX, TensorRT, and hardware-accelerated inference frameworks.
Apply modern machine learning techniques to solve challenging perception and autonomy problems across aerial and other autonomous systems operating in complex, real-world environments.
Translate cutting-edge machine learning research into production-ready capabilities by balancing model performance, robustness, computational efficiency, and operational reliability.
Partner closely with perception, autonomy, platform, and software engineering teams to integrate machine learning capabilities into mission-ready autonomous systems.
Develop benchmarks, testing methodologies, and evaluation frameworks to measure model performance, identify failure modes, and guide future improvements.
Improve training infrastructure, developer tooling, deployment workflows, and model lifecycle management to accelerate experimentation and production delivery.
Required Qualifications
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.
Proficiency of machine learning fundamentals.
Experience training and 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.
Preferred Qualifications
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).
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