Job title: Lead Machine Learning Robotics Engineer
Job description: www.offworld.ai
Lead Machine Learning Robotics Engineer
Full time position based in Pasadena, CA
OffWorld is a robotics startup working on developing a mobile robotic workforce for heavy industrial jobs on Earth, Moon, asteroids & Mars. OffWorld was founded by an experienced engineering team drawing on decades of tackling formidable engineering challenges at NASA, ESA, NOAA, Maxar Technologies, CERN, Reaction Engines, SAFRAN, Surrey Satellites and Cisco. OffWorld’s first challenge is developing and maturing a modular robotic system for extreme environments on Earth. Our robots will go deeper underground and further on Earth than humans can, eliminating the need for humans to work in life-threatening environments in mining, construction, tunneling and other dangerous industries. They will also dramatically reduce the environmental footprint and cost of operations in key economies on Earth and beyond.
At OffWorld, we see Machine Learning as the key enabler for developing sufficiently autonomous robots capable of continuously improving their performance. Recent advances in machine learning make us believe that now is the right time to apply ML that has proved itself in the digital world to the physical domain and real-world robots.
For more details on our AI approach and philosophy please see .
We are looking for an experienced Machine Learning Engineer with a strong focus in real world robotics to execute the development and integration of Machine Learning applications into OffWorld’s robots for operations in real world extreme environments. The ideal candidate has deep technical understanding of the fields of machine learning and robotics, possesses practical experience in both, and has demonstrated leadership abilities. The selected candidate will work closely with his/her peers within the ML Group and with the Robotics Mechatronics, Software and Controls Groups to identify, develop and integrate Machine Learning solutions that can be leveraged for the improved execution of robotic functions implemented at OffWorld, demonstrating improvements in performance, efficiency, computational requirements, and/or development cost.
- Work with the OffWorld Technical Groups to identify challenges in robotic applications that can be effectively tackled with ML applications
- Develop, integrate, test and deploy the proposed ML solutions into OffWorld’s robots
- Actively engage in the daily operations across teams and communicate the entry points for ML deployments and their benefits to key stakeholders throughout the organization
- Manage projects meeting milestones, significantly contributing to the development codebase of ML robotics applications
- Maintain high development standards and rigorousness of methodology
To hit the ground running, you need:
- PhD in Machine Learning / Robotics / Computer Science (or an MSc in a similar field complemented with extensive relevant industrial experience)
- Theoretical and practical knowledge of Deep Learning and Machine Learning, strong understanding of fundamental machine learning concepts
- Proficiency in classical methods and algorithms in robotics, allowing one to identify when to use an ML solution, and when a classical one
- Good Python programming skills, experience with machine learning and deep learning frameworks
- Strong understanding of software development practice
- Demonstrated experience with Linux shell, Docker, Git and other coding tools
- Familiarity with ROS and robot simulation software
- Nothing-is-impossible attitude
In a nutshell, we offer:
- Opportunity to sink your teeth into cutting edge challenges of machine learning in industrial robotics domain
- Opportunity to contribute to the mission of transforming how we mine on Earth today and how we will one day mine on the Moon, asteroids & Mars
- Opportunity to attend relevant conferences in the line of research
- Competitive compensation package
**OffWorld is a proud equal opportunity employer
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Location: Pasadena, CA
Job date: Sun, 10 Jul 2022 06:35:26 GMT
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