Senior Perception ML Engineer for Autonomous Systems

Shield AI; Washington; On-site; USD 180,000 - 240,000; Full time

Benefits offered by this job

Excellent Medical Coverage

Mental Health Employee Assistance Plan

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

Job summary

Shield AI is hiring engineers to develop and deploy state-of-the-art perception models for autonomous systems. You will own features from model development to deployment, collaborating with researchers, perception engineers, autonomy engineers, and platform teams to bring cutting-edge AI into production.

The role focuses on vision-, vision-language-, and vision-language-action models, plus scalable data pipelines and SFT workflows for mission-critical environments.

Qualifications

• Experience training and deploying ML models for computer vision in production
• Proficiency in ML fundamentals
• Experience with ML frameworks such as PyTorch and TensorFlow
• Demonstrated expertise deploying models using TensorRT and ONNX
• Proficiency in C++ and Python
• Strong analytical and problem-solving skills
• Strong understanding of 3D vision problems/algorithms
• 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
• 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)

Responsibilities

• 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

Skills

PyTorch

TensorFlow

C++

Python

3D vision

ML fundamentals

Model deployment

Education

Bachelor’s degree or higher

Tools

ONNX

TensorRT

Job description

Shield AI is hiring engineers to develop and deploy state-of-the-art perception models for autonomous systems. You will own features from model development to deployment, collaborating with researchers, perception engineers, autonomy engineers, and platform teams to bring cutting-edge AI into production.

The role focuses on vision-, vision-language-, and vision-language-action models, plus scalable data pipelines and SFT workflows for mission-critical environments.