Autonomous Robotics ML Engineer for Medical Systems

Faction Imaging Inc; New York (NY); On-site; USD 120,000 - 160,000; Full time

Job summary

Faction Imaging Inc. is building intelligence for robots to autonomously interact with the human body to collect rich medical data. We are seeking a resourceful Robotics Machine Learning Engineer to test, evaluate and improve machine learning systems on real robotic platforms.

You will work closely with experienced engineers to design experiments, benchmark new approaches, and improve autonomy and reliability. The role is based in Manhattan, NY, and requires significant on-site time in our lab.

Qualifications

• Experience operating and testing autonomous robotic systems in a lab environment.
• Experience collecting and analyzing data with statistical methods.
• Experience with RGB cameras, depth cameras, force sensors, tracking systems.
• Experience with ROS 2 and real-time publish/subscribe communication.
• Experience with multi-threading and asynchronous Python.
• Experience with modern ML methods for robotic control (behavior cloning, RL).
• Extensive experience with Python and ML libraries.
• Experience in autonomous navigation is a major plus.
• Familiarity with real-time robotic control systems is a plus.

Responsibilities

• Test, evaluate and improve ML systems on real robotic platforms.
• Design experiments and benchmark new approaches.
• Improve performance and reliability of autonomous systems.

Skills

Autonomous robotics

Data analysis

ROS 2

Multithreading

Python ML

Sensing hardware

Reinforcement learning

Tools

PyTorch

TensorFlow

scikit-learn

Job description

Faction Imaging Inc. is building intelligence for robots to autonomously interact with the human body to collect rich medical data. We are seeking a resourceful Robotics Machine Learning Engineer to test, evaluate and improve machine learning systems on real robotic platforms.

You will work closely with experienced engineers to design experiments, benchmark new approaches, and improve autonomy and reliability. The role is based in Manhattan, NY, and requires significant on-site time in our lab.