Machine Learning Engineer

AMP is applying AI-powered sortation at scale to modernize the world's recycling infrastructure and maximize the value in waste. AMP gives waste and recycling leaders the power to harness AI to reduce labor costs, increase resource recovery, and deliver more reliable operations. With hundreds of deployments across North America, Asia, and Europe, AMP’s technology offers a transformational solution to waste sortation and changes the fundamental economics of recycling.

Headquartered in Louisville, Colorado, the Denver Post and Built In Colorado have recognized AMP as one of the state's top workplaces. AMP has operations and career opportunities outside of Atlanta, Cleveland, Portsmouth, Virginia, and Europe. We’re fostering an environment where passionate individuals can grow and create impact. We seek unconventional thinkers to join our mission to enable a world without waste;

at AMP, your contributions have meaning and can spur change. With backing from top-tier investors and national recognition including North American Cleantech Company of the Year, we’re always seeking ways to better our operations, raising the bar on innovation, and looking to collaborate and improve in what we do. Learn more at

AMP Robotics is hiring a Machine Learning Engineer reporting to the Engineering Manager of Perception to focus on developing our deep learning and other machine learning models and shipping them to production. AMP doesn’t just do machine learning, we are driven forward by it. Our core technology revolves around deep-learned models applied to robotics domains, and we are always striving to improve our performance.

In pursuit of this, AMP is looking for a highly skilled individual to join our machine learning team and aid in building out cutting edge computer vision technology. In this role, you would be an individual contributor on our perception team, working on deep learning and computer vision research and development projects to help us implement state of the art deep learning techniques, and helping to develop new applications by working hands-on with data, modeling, and evaluation.

You would aid in bringing models from ideation all the way through to production, and help us to maintain our technological edge.

• Experiment with modern neural network architectures or techniques driven by research publications.
• Design and implement machine learning perception solutions in new domains.
• Design and execute experiments to validate R&D deep learning approaches.
• When experiments have positive results, design a path to production to deliver impact to our facilities.
• Collaborate with AI Data Team project managers, ML modeling engineers, and Cloud infrastructure engineers.
• Serve as a subject matter expert for experiment design and statistical modeling for the Software organization, applying these skills to non deep learning projects when relevant.
• Help us support and improve our ML infrastructure.

The successful candidate will have:

• Master’s degree in Computer Science/Machine Learning or similar, or equivalent combination of technical education and work experience.
• 4+ years' experience writing production-level code in python.
• 4+ years of experience implementing statistical models in production environments.
• Proficiency with professional software engineering practices; including coding standards, code reviews, source control management, build processes, testing, and operations.
• Strong familiarity with PyTorch.
• Strong familiarity with experiment design and experiment execution.
• Research fluency with a deep learning domain.

• 3+ years of experience with deep learning, particularly computer vision.
• Proficiency with neural network quantization, acceleration, and inference tools, particularly TensorRT.
• Familiarity and interest in research-level mathematics.
• Proficiency working with SQL databases and data pipelines.
• Experience deploying large-scale and small-scale models in low-latency environments.
• Startup ready mentality.
• Passion for recycling, robotics and changing the world.

• Prolonged periods of sitting at a desk and working…