Remote Machine Learning Engineer — Perception & Production

AMP: AI-Powered Sortation for Waste and Recycling; United States; On-site; USD 162,000 - 170,000; Full time

Benefits offered by this job

Medical insurance

Dental and Vision insurance

401(k) plan

Flexible Time Off

Paid sick time

Paid holidays

Job summary

AMP Robotics is seeking a Machine Learning Engineer to join the Perception team, building and shipping deep learning models to production. You will work on vision-based analytics, experiment design, and deploying models that improve facility performance.

The role emphasizes collaboration with data, ML, and cloud teams, with remote/hybrid options and a path to meaningful impact in recycling and waste management.

Qualifications

• Master’s degree in Computer Science or Machine Learning or equivalent.
• 4+ years writing production-level Python code.
• 4+ years implementing statistical models in production environments.
• Proficiency with professional software engineering practices (code reviews, version control, testing, deployment).
• Strong familiarity with PyTorch.
• Strong familiarity with experiment design and execution.
• Research fluency in deep learning.

Responsibilities

• Experiment with modern neural network architectures and publications-driven techniques.
• Design and implement ML perception solutions in new domains.
• Design experiments to validate R&D deep learning approaches.
• Bridge research to production to deliver impact to facilities.
• Collaborate with AI Data Team, ML engineers, and Cloud infra engineers.
• Serve as SME for experiment design and statistical modeling.
• Help maintain ML infrastructure.

Skills

Python

PyTorch

Experiment design

Statistical modeling

Production code

Education

Master’s degree in Computer Science / Machine Learning

Tools

TensorRT

SQL databases

Docker

Git / CI-CD

Job description

AMP Robotics is seeking a Machine Learning Engineer to join the Perception team, building and shipping deep learning models to production. You will work on vision-based analytics, experiment design, and deploying models that improve facility performance.

The role emphasizes collaboration with data, ML, and cloud teams, with remote/hybrid options and a path to meaningful impact in recycling and waste management.