Senior ML Engineer (AI Research, Physical AI)

Vedi l’offerta su Go Lavoro: golavoro.it/annuncio-lavoro/7by6i9

ppbThis position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior ML Engineer (AI Research, Physical AI) based in Italy. /b /p pThis role offers the opportunity to contribute to cutting-edge AI research focused on creating intelligent systems capable of interacting with the physical world.br/ You will work on advanced machine learning challenges involving robotics, multimodal models, reinforcement learning, and embodied intelligence.br/ The position combines research innovation with practical engineering, transforming experimental ideas into reliable AI capabilities.br/ You will collaborate with world-class researchers and engineers to develop scalable solutions across simulation, data, and real-world robotic systems.br/ The role requires deep technical expertise, curiosity, and the ability to move quickly from hypotheses to impactful prototypes.br/ This is an opportunity for an experienced ML professional to shape the future of AI-driven physical systems in an international environment. /p bAccountabilities: /b pThe role focuses on researching, designing, and implementing advanced machine learning solutions for physical AI applications. The professional will develop models, algorithms, and infrastructure that enable intelligent agents to perceive, reason, and act in real-world environments while collaborating with multidisciplinary research and engineering teams. /p ul liDesign, implement, train, and evaluate large-scale machine learning models and algorithms for robotic agents. /li liDevelop vision-language-action architectures that connect multimodal perception, language understanding, and physical control. /li liResearch and apply reinforcement learning, imitation learning, and learning-from-demonstration techniques for complex robotic tasks. /li liBuild scalable approaches for incorporating human demonstrations, simulation data, video, and autonomous robot experiences into AI models. /li liCreate datasets, evaluation methodologies, data-quality pipelines, and capture strategies for embodied learning systems. /li liDevelop simulation environments and conduct sim-to-real experiments on robotic platforms. /li liExplore planning methods, guided generation, and action trajectory optimization for intelligent agents. /li liPrototype capabilities in areas such as dexterous manipulation, mobile robotics, whole-body control,



and general-purpose robotic systems. /li liDevelop robust research software and distributed training infrastructure to accelerate experimentation. /li liCollaborate with research, infrastructure, and engineering teams to transform experimental results into reliable solutions. /li liCommunicate research outcomes through technical documentation, publications, demonstrations, and open-source contributions. /li liContribute to technical strategy by identifying new research opportunities and advancing AI capabilities. /li /ul h3Requirements: /h3 pThe ideal candidate is a senior machine learning professional with strong research experience and advanced engineering capabilities. The role requires deep knowledge of AI foundations, experience with large-scale model training, and the ability to independently formulate, test, and deliver innovative research solutions. /p ul liStrong theoretical understanding of machine learning, reinforcement learning, robotics, or related AI disciplines. /li liDeep expertise in at least one relevant area, including: ul liReinforcement learning. /li liImitation learning. /li liMultimodal generative modeling. /li liComputer vision. /li liRobotics. /li liPlanning and control systems. /li /ul /li liExperience training and evaluating modern deep learning models, including transformer-based or multimodal foundation models. /li liSignificant experience training large-scale models across multiple computational nodes. /li liStrong software engineering and algorithm development skills, primarily using Python. /li liExperience with modern deep learning frameworks, particularly JAX or equivalent technologies. /li liAbility to design rigorous machine learning experiments, analyze results, and draw meaningful conclusions. /li liExperience rapidly iterating between modeling, data, infrastructure, and evaluation approaches. /li liStrong communication skills and ability to collaborate across research and engineering teams.



/li liAbility to document research findings clearly and contribute to technical reports or scientific publications. /li liExcellent command of English, including technical writing and presentations. /li liFamiliarity with software engineering practices such as version control, testing, code reviews, and CI/CD. /li /ul pbNice-to-have qualifications: /b /p ul liExperience working with physical robots and robotic simulation environments. /li liBackground in dexterous manipulation, humanoid robotics, mobile manipulation, or whole-body control. /li liExperience with multimodal sensing technologies, including tactile, force-torque, depth, or proprioceptive signals. /li liExperience collecting human demonstrations through teleoperation, motion capture, wearable devices, or observation. /li liExperience developing or fine-tuning vision-language models, vision-language-action models, video models, or world models. /li liKnowledge of deep reinforcement learning methods such as offline RL, PPO, actor-critic methods, reward modeling, preference learning, or model-based RL. /li liFamiliarity with robotics frameworks and simulators such as MuJoCo, Isaac Sim, Isaac Lab, PyBullet, ROS, or similar tools. /li liExperience with distributed training techniques including FSDP, ZeRO, FlashAttention, mixed precision training, quantization, and distributed checkpointing. /li liPhD in Computer Science, Robotics, Machine Learning, Artificial Intelligence, or a related technical field, or equivalent practical experience. /li liTrack record of impactful research publications, open-source projects, or deployed robotic systems. /li liExperience building large-scale data processing, simulation, or model training systems. /li liExperience delivering research prototypes or products in fast-paced, innovation-driven environments. /li /ul h3Benefits: /h3 ul liCompetitive compensation package. /li liFlexible work environment with autonomy and ownership. /li liOpportunity to work on advanced AI research projects with real-world impact. /li liCareer growth opportunities and continuous learning support. /li liCollaboration with talented international research and engineering teams. /li liInnovative culture focused on experimentation, bold ideas, and meaningful technological progress. /li liOpportunity to influence the future development of AI-powered physical systems. /li /ul /p #J-18808-Ljbffr

Vedi l’offerta su Go Lavoro: golavoro.it/annuncio-lavoro/7by6i9