Top Tech Jobs & Startup Jobs

The Senior Project Engineering Manager oversees the technical execution of robotic systems, coordinating across teams and managing hardware delivery from concept to acceptance, ensuring project milestones are met and issues debugged.

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.

As a Robot QA Specialist, you will test new features, identify bugs, run stability tests, and collaborate with engineers to improve robot performance.

The Senior Software Engineer will work on robotics software, solving physical manipulation challenges, designing complex systems, and leading technical initiatives.

Own outbound prospecting across Spain, Italy and Portugal to build a strong sales pipeline. Collaborate with marketing to identify and nurture prospects, map target accounts and stakeholders, create personalized outreach (LinkedIn, InMail, email, calls), represent Nomagic at trade shows, and optimize funnel conversion.

Lead end-to-end sales for warehouse automation in Spain, Italy and Portugal. Identify and close customers, run solution design workshops and reference visits, develop partner collaborations, support upsell and trade-fair presence, and improve funnel conversion to drive adoption of robotic pick-and-place SaaS solutions.

Develop and productionize deep learning methods for robotic perception and manipulation. Build, evaluate, and monitor ML models in production, automate retraining pipelines, detect anomalies and influence data-driven company strategy while collaborating with hardware and engineering teams.

Own Iberia sales for warehouse automation solutions from lead generation to close. Drive customer adoption, run workshops, negotiate contracts, build partner relationships, support trade shows, and improve funnel conversion.

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.