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Design and integrate automated robotic cells and production-line solutions, including electrical, mechanical, pneumatic, control, safety, and software components. Create PLC programs, HMI systems, prototypes, and documentation; debug, maintain, and service robots at customer sites. Collaborate with software engineers, contractors, and cross-functional partners, manage component procurement, and support robot deliveries. Travel to customer sites up to 20% of the time.

As a Research Scientist, you will advance physical AI by merging ML research with robotics, focusing on foundational model training, adaptation, and real-world evaluation in factory environments.

The Research Engineer will bridge ML research and industrial robotic execution, focusing on training models, designing benchmarks, and improving real-world performance through large-scale infrastructure and evaluations.

The Senior Business Development Representative will generate leads, qualify prospects, and collaborate with marketing and engineering teams to grow Nomagic's U.S. customer base, using an iterative outreach approach and attending trade events.

The Senior Project Manager will oversee robot deployment projects, coordinate teams, ensure project delivery, manage budgets, and maintain client communication in the intralogistics and robotics industry.

Conduct research at the intersection of ML and robotics to design and train foundation multimodal models from real-world robotic deployment logs. Work on pretraining and post-training (fine-tuning, RL/IL, RLHF), develop scalable data mixtures and training curricula, run ablations and scaling studies, collaborate with ML infrastructure for distributed training, build physical evaluation benchmarks, and iterate on real-robot deployments to improve robustness and performance.

Build, train, and deploy large-scale multimodal robot foundation models using production deployment logs; develop training infrastructure, experiment tooling, and on-robot evaluation pipelines; design physical benchmarks and close the loop between real-world results and ML research.