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Team Lead, Embodied AI & Robotics | DACH
Paddy leads DeepRec.ai's Embodied AI and Robotics team across Europe. Over four years he has placed AI and computer vision engineers everywhere from early-stage startups to global enterprises.
paddy.hobson@deeprec.ai+49 30 2295 7671
Recruitment Consultant
Harry works alongside Paddy in the Embodied AI and Robotics team, focusing on robotics, embodied AI and computer vision hiring across Europe.
harry.brant@deeprec.ai+41 41 562 95 93
A snapshot of the senior robotics and AI talent we're working with. There are plenty more where these came from.
AI Engineer (VLM/LLM)
Embodied AI for humanoids
AI Engineer
Robot learning
Staff Robotics Engineer
Humanoid robotics software
Senior Machine Learning Engineer
Speech synthesis and ML
Member of Technical Staff
ML and sensor fusion
Senior Research Scientist
Robotics and spatial AI research
LLM Inference Engineer
LLM and generative model inference
Research Manager
Deep learning research
More where that came from
Tell us what you're hiring for and we'll put together a shortlist for your team.
Ask PaddyAnonymised profiles of people we've placed with clients. Tap a card for the full profile.
Robotics engineer with doctoral research experience spanning compliant manipulation, real-time control and learning algorithms. Experience includes hardware validation, safety-aware motion planning, learning from demonstration, and the integration of VLA models and diffusion policies.
Motion planning & redundancy resolutionVariable impedance controlRobot learningPhysical human-robot interactionTeleoperation & shared controlReal-time systems
Robotics company, AI division | 2025 - Present
University robotics research group | 2019 - 2024
Working Student - Robotics & Mechatronics
Research institute
University production systems institute | 2016 - 2017
Distinction Summa Cum Laude. Research focused on variable impedance control for compliant robot interactions, energy-based methods, learning from demonstration and dynamical systems.
Research focused on synchronising primary and auxiliary tasks in redundant manipulators. Group project: motion-capture control of a six-DOF robot arm using ROS.
Research focused on electromagnetic motion control for drug-delivery applications.
Programming MATLAB/Simulink, C++, Python, C, C#, Java, R
Robotics & vision ROS, Gazebo, OpenCV
Engineering & development CMake, Docker, Git, Linux, SolidWorks
Productivity Notion
Lightweight and collaborative robot arms; six-DOF industrial manipulators; haptic interfaces; force/torque sensors; robotic soft hands; microcontrollers and single-board computers.
Software project management and Professional Scrum Master training
Working effectively in international academic environments
PLC and SCADA integration training, Levels 1 and 2
ArabicEnglish
Robotics engineer with experience in sensor-driven environment modelling, control theory and real-world system integration. Focus areas include autonomous exploration, semantic mapping, scene understanding, collision-free planning and motion control for industrial manipulators.
Next-Best-View exploration3D reconstructionSemantic mappingRedundancy resolutionQP-based motion planningSensor integrationRobotic control
Industrial robotics company | 2022 - Present
Industrial robotics company | 2021
Industrial robotics company | 2020 - 2021
Research in vision-driven environment modelling for industrial robotics: multimodal datasets for SLAM and novel view synthesis; depth-based Next-Best-View exploration; semantic mapping; and sensorless workspace modelling.
Graduated 2023 | Highest honours
Thesis research in constrained robot redundancy resolution. Coursework included robotics fundamentals, field and service robotics, modelling and simulation, nonlinear control, real-time systems and virtual prototyping.
Graduated 2019 | Highest honours
Thesis research in robotic navigation simulation and aerial-robot stabilisation. Coursework included systems theory, object-oriented programming, PLC programming, linear control, signal processing and electromechanics.
Robotics Kinematics, dynamics, redundancy resolution, path planning, motion control
Computer vision 3D reconstruction, image segmentation, vLLM-based classification, semantic mapping Programming & development: C++, Python, CMake, MATLAB, Docker, Git, Azure DevOps Simulation & frameworks: Simulink, Gazebo, ROS, ROS 2, proprietary industrial robotics frameworks Productivity: Office applications, LaTeX
Publication and patent contributions spanning constrained redundancy resolution, exploratory workspace modelling, neural rendering and SLAM evaluation, and robot workspace boundaries.
Agile development, deliverable planning, cross-functional collaboration, structured project execution, technical documentation, mentoring and stakeholder communication.
Italian - NativeEnglish - AdvancedGerman - Basic
Robotics engineer bridging imitation and reinforcement learning with real-time control to deploy robust manipulation systems. Experience spans end-to-end data collection, learning pipelines and control stacks under constrained data and latency budgets, alongside technical leadership and applied research.
Imitation & reinforcement learningReal-time controlRobotic manipulationHaptic teleoperationData-efficient learningModel predictive controlSystems integration
Robotics company | 2025 - Present
Industrial technology company, corporate research | 2018 - 2025
University robotics research group | 2017 - 2018
University robotics research group | 2014 - 2017
PhD - Control Theory
Completed 2014
M.Sc. - Mechatronics in Mechanical Engineering
Designed and developed hardware and software for a commercialised, low-cost pick-and-place robot through thesis research and freelance work.
Robotics Control systems, model predictive control, inverse dynamics and kinematics, motion planning, computer vision, industrial robots Robot learning: Reinforcement learning, imitation learning, self-supervised learning Programming & frameworks: C++, Python, PyTorch, MATLAB, ROS, ROS 2 Simulation: Gazebo, MuJoCo, Isaac Sim Development operations: CI/CD tools, Git, Docker
Technical leadership, cross-functional team management, research supervision, project coordination, user-experience research and translation of customer requirements into deployed robotics systems.
Publication and patent contributions across robot learning, manipulation, grasping, teleoperation and control. Experience securing research funding and delivering collaborative research programmes.
Machine learning researcher developing world models, learned dynamics and imitation-learning methods for efficient robot skill acquisition. Experience connects simulation-based training with real-hardware deployment, alongside research leadership, student supervision and robotics laboratory development.
World modelsSim-to-real transferImitation learningModel-based reinforcement learningProbabilistic modellingIntrinsic motivationHardware validation
Humanoid robotics company | 2025 - 2026
Automotive company, research group | 2017 - 2024
University research group | 2014 - 2016
University research group | 2011 - 2014
2014 - 2020 | Summa Cum Laude
Doctoral research in unsupervised control, efficient inference for time-series modelling and intrinsic motivation.
M.Sc. - Robotics, Cognition & Intelligence
2012 - 2014
B.Sc. - Computer Science
2009 - 2012
Machine learning World models, learned dynamics, model-based reinforcement learning, imitation learning, behaviour cloning, probabilistic modelling, intrinsic motivation Robotics: Sim-to-real transfer, robot experiments, learned-policy validation, control theory, tactile sensing Programming & frameworks: Python, PyTorch, C++ Systems: Embedded systems, FPGA-based sensor readout, computer vision, hardware/software integration
Emphasises the connection between mathematical foundations and practical engineering. Advocates hands-on learning through real robots, sensors, custom electronics and deployment of learned controllers.
GermanEnglish
Robotics and AI engineer specialising in reinforcement learning, imitation learning and autonomous systems. Experience building GPU-accelerated learning pipelines and deploying learned policies on mobile robots and nano-drones, alongside autonomous-driving simulation and perception development.
Deep & multi-agent RLImitation learningSim-to-real transferGPU simulationAutonomous drivingEmbedded visionSensor fusionROS 2
Autonomous-driving company | 2025 - Present
Robotics company | 2024 - 2025
Artificial intelligence research institute | 2024
University autonomous-racing team | 2022 - 2024
University research group | 2023
2022 - 2024 | Highest honours
Core subjects ML, deep learning, robot learning, generative AI, computer vision, NLP, optimisation and big data.
B.Sc. - Computer Engineering
Core subjects algorithms, data structures, object-oriented programming, computer architecture and control.
Advanced programming Python, C/C++, SQL, Bash
Academic programming Java, JavaScript, MATLAB, PHP, x86 Assembly
Reinforcement learning Deep RL, multi-agent RL, PPO, SAC, sim-to-real
Imitation learning Behaviour cloning, DAgger
Vision & models Object recognition and detection, monocular depth, VLA models, world models, VAEs, GANs, diffusion models Model optimisation: Neural architecture search, quantisation
Simulation Isaac Sim, Isaac Lab, Warp, CARLA, Madrona, GPUDrive, Gazebo, MuJoCo, Webots ML & robotics: PyTorch, ROS 2, SKRL, Stable-Baselines3, Gymnasium, vectorised environments, Ultralytics Data & scientific computing: Pandas, NumPy, scikit-learn, SciPy, Statsmodels, Matplotlib, Hadoop, Spark
Git, GitHub, Docker, Singularity, CI/CD, GitHub Actions, Linux, REST APIs, Flask, AJAX, Gunicorn, Apache Server, InfluxDB, MongoDB.
English - C2Romanian - C2Italian - C2French - A1
Engineering leader with doctoral training in control and experience in robot learning and scalable data infrastructure. Leads humanoid control, simulation, data pipelines and the Vision-Language-Action (VLA) model lifecycle.
Robotics company | 2026 - Present
Robotics company | 2025 - 2026
Robotics company | 2024 - 2025
University research institute | 2018 - 2023
Automotive technology company | 2015 - 2018
Doctor of Engineering
Research in data-driven, self-learning thermal management and hybrid optimal control for electric powertrains. Doctoral work addressed energy and service-life management in electric vehicles.
M.Sc. - Energy Technology & Thermophysics
2012 - 2015
B.Sc. - Energy Technology & Thermophysics
2008 - 2012
AI & robot learning Diffusion policies, reinforcement learning, VLA pre-training, model post-training Control: Model predictive control, hybrid optimal control, dynamic programming, geometric fabrics Robotics & infrastructure: ROS 2, MuJoCo, Docker, Kubernetes, teleoperation, whole-body control, data collection and validation Systems: Sensor-actuator-control integration, hardware-in-the-loop, microcontrollers, system-on-chip platforms, CAN, LIN, UART Automotive: Electric powertrains, thermal management Programming & frameworks: MATLAB, Python, C++, PyTorch
Published research on predictive energy control and data-driven thermal management; further work on hybrid optimal-control methods submitted for review. Presented research at control and electric-mobility conferences.
Chinese - NativeGerman - C1English - C1
Research scientist and doctoral candidate with more than five years of experience in robotic systems spanning machine learning and foundation models. Focuses on interactive learning from demonstration, multimodal skill adaptation and continual learning, with real-robot deployment and industry research collaboration.
LLM/VLM/VLA integrationInteractive robot learningLearning from demonstrationContinual learningHuman-robot interactionSynthetic dataSim-to-real transfer
Robotics research institute | 2020 - Present
University computing and technology department | 2024 - Present
Robotics research institute | 2019 - 2020
Industrial technology company | 2017 - 2018
Industrial instrumentation company | 2016 - 2017
Industrial technology company | 2011 - 2014
Rotations across human resources, accounting and IT.
2020 - Present | In progress
Specialisation in machine learning and artificial intelligence.
2018 - 2020 | Data Science specialisation | Top of class
2014 - 2018 | Top of class
International Study Semester - Business Diploma
2018
Study areas data science, IT and international business.
Programming Python (5+ years), C++, Java, Bash
ML & robotics PyTorch, TensorFlow, JAX, scikit-learn, ROS, ROS 2, LLM/VLM integration, reinforcement learning, imitation learning Research areas: Robot learning, human-robot interaction, multimodal skill learning, foundation models, sim-to-real, offline reinforcement learning, continual learning, computer vision Development & deployment: Git, Jenkins, CI/CD, Docker, cloud-based ML training, Linux Additional tools: LaTeX, Blender, PyCharm, VS Code
Cross-team research and engineering collaboration, industry partnerships, European research programmes, doctoral-community representation and teaching support in machine learning for robotics.
AI researcher with a completed doctorate and experience in tactile foundation models, multimodal fusion and robotic perception. Develops learning pipelines connecting touch, vision and language, with applications in dexterous manipulation, teleoperation, pose estimation and industrial inspection.
Tactile learningMultimodal fusionKnowledge distillationDexterous manipulationImitation learningComputer visionReal-time inference
Robotics company | 2025 - Present
Industrial AI company | 2023 - 2024
University research laboratory | 2023
Further visiting-research experience at a university robotics laboratory, alongside doctoral work in artificial intelligence.
Ph.D. - Artificial Intelligence / Computing
2020 - 2024
Awarded a teaching and research scholarship.
2016 - 2019 | First-class honours
Automated Surface Inspection & Repair
Maintenance Scheduling & Outdoor Inspection
Semiconductor Defect Inspection
Programming Python, MATLAB, TypeScript, Node.js
Deep learning PyTorch, TensorFlow, transformers, knowledge distillation, multimodal learning, vision-language models Vision & robotics: Tactile sensing, hand retargeting, teleoperation, RGB-D pose estimation, segmentation, YOLO, nnU-Net, PCA, RANSAC Application development: React, GraphQL, cloud data storage
Publication contributions in attention-based segmentation, defect measurement, 3D surface inspection and repair-path optimisation. Experience includes innovation competition recognition, early-stage venture programmes and grant awards.
Robotics engineer with a PhD and expertise in manipulation and robot learning, spanning perception, teleoperation, demonstrations, policy evaluation and physical deployment. Experience includes bimanual manipulation, tactile grasping, data-efficient learning and industrial robotic perception.
Diffusion & flow-matching policiesImitation learningHuman-in-the-loop RLBimanual manipulationRGB-D perceptionCalibrationSimulated and physical evaluation
Robotics company | 2025 - Present
Applied industrial research institute | 2024 - 2025
University and robotics research institute | 2019 - 2023
Visiting robotics researcher (2022); robotics teaching (2021); industrial vision (2019); robotic agricultural manipulation (2018); robot vision and human-robot interaction (2017).
Completed 2023 | Machine Learning & Robotics
Completed 2018 | Master's-equivalent qualification
Behaviour cloning, Action Chunking Transformer (ACT), pi0/pi0.5, diffusion and flow-matching policies, flow Q-learning (FQL), ACFQL, HIL-SERL, offline and online reinforcement learning.
RGB-D and 3D vision, tactile perception, SE(3) calibration, hand-eye and tool-centre-point calibration, teleoperation, bimanual manipulation, ROS, ROS 2, MoveIt, MuJoCo and Isaac Sim.
Python, PyTorch, Docker, Linux, pytest, Git, GPU profiling, torch.compile, CUDA graphs and mixed-precision training.
Collaborative and industrial robot arms, mobile and humanoid platforms, stereo and RGB-D cameras, tactile sensors, teleoperation interfaces and VR headsets.
Controlled policy benchmarking, multi-seed comparisons, simulation-to-hardware validation, human-in-the-loop corrections, real-episode replay, automated quality annotation and cross-functional technical leadership.
French - NativeEnglish - Fluent
Physical AI engineer with experience across industrial, medical and automotive robotics, building on a background in computational design and fabrication. Specialises in humanoid teleoperation, synthetic data and sim-to-real learning, spanning DNN perception, reinforcement learning and multimodal VLA foundation models.
VLA foundation modelsHumanoid manipulationTeleoperation & XRSynthetic dataSim-to-real learningDigital twinsMultimodal perceptionRobotic control
Automotive company, advanced technology laboratory | 2025 - Present
Automotive company | 2024 - 2025
Medical technology company | 2023 - 2024
Medical technology company | 2021 - 2023
Robotics company | 2018 - 2020
University research group | 2018 - 2020
Researched large-scale robotic fabrication, with machine-learning-based manufacturing optimisation and structural testing.
Robotic manufacturing company | 2017 - 2018
Developed robotic manufacturing automation using computational and learning-based optimisation for production-scale fabrication.
Design and product development company | 2016 - 2017
Worked on computational design and product development using parametric workflows, simulation and early ML-based exploration for design optimisation and digital fabrication.
Master of Science
Bachelor of Science - Computer Science
Vision-Language-Action foundation models, multimodal fine-tuning, reinforcement learning, DNN perception and pose estimation, motion retargeting, world models and embodied AI.
Isaac Lab, Omniverse, MuJoCo, synthetic dataset generation, digital twins, simulation-to-reality transfer and demonstration-data pipelines.
Humanoid robotics, teleoperation, XR interfaces, ROS, ROS 2, Unity3D, real-time robotic interaction, trajectory planning and embedded deployment.
C++, Python, multimodal sensor fusion, six-degree-of-freedom object tracking, CT and point-cloud integration, and deep-learning-based machine vision.
Technical leadership across humanoid Physical AI and robotic digital twins. Experience connecting data collection, model training, simulation and physical deployment, with patent contributions in robotic interaction and control.
Research scientist with doctoral training and more than eight years of experience in generative modelling, spanning diffusion, flow matching and GANs. Combines mathematical model development with distributed training, multimodal learning and applications in robotics and medical imaging.
Generative modelsMultimodal foundation modelsFlow matchingDiffusionOptimal transportDistributed trainingVision-language learning
Robotics company | 2026 - Present
University hospital research group | 2024 - 2025
Research institute | 2019 - 2024
University research group | 2017 - 2019
Doctor of Philosophy (Ph.D.)
Doctoral research across a research institute and university graduate programme, with work in generative modelling and medical imaging.
2017 - 2019 | Aeronautical Engineering specialisation
B.Sc. - Mechanical Engineering
2013 - 2017
Experience translating mathematical formulations into distributed implementations, including training across 16 accelerator nodes using Fully Sharded Data Parallel (FSDP). Work spans model architecture and training dynamics.
Programming Python, MATLAB, R, C/C++, Rust, Julia
ML frameworks & inference PyTorch, JAX, Caffe, vLLM
Development & infrastructure Nsight, Git, Docker, SLURM, pytest, cloud object storage and compute Research areas: Generative models, robotics, optimal transport, differential geometry, 3D vision, multimodal learning
Chinese - NativeEnglish - C2German - A2
Research scientist with doctoral training in control science and experience spanning VLA deployment, generative motion planning, Gaussian-process learning and multi-agent control. Combines real-robot development with research leadership, student supervision and applied machine learning.
Robotics company | 2026 - Present
University robotics institute | 2025
Public-health research institute | 2023 - 2024
Data-science training programme | 2022 - 2023
University control research group | 2019 - 2022
University intelligent-systems research group | 2017 - 2022
2016 - 2017
Tested and analysed fibre-optic current transformers at an electrical engineering organisation.
2014
Worked on CNC lathe programming and quality inspection at an industrial manufacturer.
Research across machine learning, nonlinear and underactuated control, model predictive control, robotics and multi-agent coordination.
M.Sc. - Mechanical & Electrical Engineering
B.Sc. - Electrical Engineering & Automation
Robot learning & control VLA policies, generative models, diffusion/flow matching, reinforcement learning, Gaussian processes, MPC, nonlinear control, teleoperation, distributed learning Programming: Python, MATLAB/Simulink, Julia, C++, JavaScript, TypeScript, SQL, HTML/CSS ML: PyTorch, JAX, TensorFlow, Keras, scikit-learn, Hugging Face, Transformers, PEFT, TRL, Unsloth, statsmodels, spaCy, NLTK, Gensim Robotics: Isaac Sim/Lab, MuJoCo, ROS, Gazebo, LeRobot, OpenCV, motion capture Web & data: React, Next.js, Vite, Django, Flask, FastAPI, MySQL, PostgreSQL, NumPy, Pandas Visualisation: D3.js, Vega-Lite/Altair, Plotly, Matplotlib, seaborn, GeoPandas Engineering: Git, Docker, cloud deployment, experiment tracking, AI-assisted development tools
Teaching and supervision of more than ten student theses. Extensive publication and patent contributions in safe robot learning, distributed Gaussian processes, multi-agent coordination and autonomous systems. Academic service includes editorial duties, conference session leadership and peer review. Recognition includes research-paper finalist selections, innovation awards and academic scholarships.
Chinese - NativeEnglish - Full professional proficiencyGerman - Beginner