Senior ML Engineer, Vision & Autonomous Systems

F7d8749a; Washington (District of Columbia); On-site; USD 150,000 - 210,000; Full time

Benefits offered by this job

Bonus

Equity

Benefits

Job summary

Shield AI in the United States is seeking a senior ML engineer for the Hivemind Solutions Perception team to advance vision-language models for autonomous systems. You’ll design, train, and deploy state-of-the-art models from development to production, working with researchers and engineers across platforms.

Responsibilities include building scalable data pipelines, training workflows, and deploying models on embedded hardware using ONNX, TensorRT, PyTorch, and TensorFlow.

Qualifications

• Typically requires 5+ years of related experience with a Bachelor’s degree; or 4 years and a Master’s; or 2 years with a PhD.
• Proficiency in ML fundamentals and production CV deployment.
• Experience deploying ML models for computer vision in production.

Responsibilities

• Design, train, fine-tune, and maintain state-of-the-art vision, vision-language, and vision-language-action models for autonomous systems.
• Build scalable data pipelines, supervised fine-tuning workflows, and evaluation loops for improvement.
• Deploy and optimize models on embedded hardware using ONNX, TensorRT, and hardware-accelerated inference.
• Collaborate with perception, autonomy, platform, and software teams to integrate ML capabilities.
• Develop benchmarks, testing methodologies, and evaluation frameworks to measure model performance.

Skills

ML fundamentals

C++

Python

Problem solving

Communication

Education

Bachelor's degree in CS/EE

Tools

PyTorch

TensorFlow

ONNX

TensorRT

Job description

Shield AI in the United States is seeking a senior ML engineer for the Hivemind Solutions Perception team to advance vision-language models for autonomous systems. You’ll design, train, and deploy state-of-the-art models from development to production, working with researchers and engineers across platforms.

Responsibilities include building scalable data pipelines, training workflows, and deploying models on embedded hardware using ONNX, TensorRT, PyTorch, and TensorFlow.