Paddy Hobson

About the Company We’re supporting a venture-backed AI company building cutting-edge surgical AI systems focused on video understanding. Their initial product transforms surgical videos into structured clinical reports - a focused entry point into a much bigger vision: developing a large-scale surgical foundation model, ultimately enabling advanced perception systems and autonomous surgical robotics.This is a deep-tech team building models from first principles - not treating AI as a black box. They combine ambitious research goals with real-world deployment in high-stakes medical environments. The Role Our client is hiring a Computer Vision Engineer who operates at the intersection of research and production. This is not a pure research role - and not just product engineering. They’re looking for someone who can:Deliver research-grade innovationWrite clean, scalable, production-ready codeMove fluidly between experimentation and deploymentYou will work on state-of-the-art video understanding systems that convert unstructured surgical footage into structured intelligence. This role is central to their long-term roadmap toward advanced autonomy. What You’ll Work OnDesigning and training advanced video understanding modelsExtending image-based CV architectures into temporal domainsWorking with multimodal and potentially 3D data (point clouds beneficial)Building scalable training pipelines, including distributed trainingBridging research prototypes into production systemsContributing to publications at leading AI conferencesCollaborating closely with a highly technical founding teamWhat They’re Looking For Technical BackgroundStrong foundation in Computer Vision on imagesExperience in video understandingExposure to 3D data / point clouds (beneficial)Experience with model training pipelines and optimizationAbility to implement research papers quickly and robustlyStrong software engineering fundamentalsResearch MindsetTrack record (or clear potential) for top conference-level workAbility to derive models from first principlesDeep understanding of modern CV architecturesProblem-SolvingComfortable working in ambiguous environmentsStrong analytical and structured thinking skillsAble to tackle unfamiliar domains effectivelyCollaboration & Product AwarenessUnderstands real-world constraints and client needsComfortable working closely with cross-functional teamsThrives in a collaborative environment (not a solo contributor role_Why Consider This Opportunity?Meaningful Impact - Building AI systems that support safer surgical procedures and improve access to care. Big Technical Vision - Report generation is the entry point. The broader roadmap includes foundational models and advanced autonomy systems. Genuine Deep Tech - This team is building core models and infrastructure from the ground up. Publication & Credibility - Publishing at leading conferences is part of the company’s DNA. Strong Talent Density - You’ll work alongside highly technical peers in an ambitious, research-driven environment.

Location: Munich (Hybrid/On-site depending on team setup) Type: Full-time Company Overview We are working with a fast-growing Vision / AI company building production software for the food and retail industry. Their systems help customers reduce food waste and improve operational efficiency - supporting sustainability goals through real-time computer vision and automation. With teams across Europe, the US, and Asia, we combine startup pace with real-world deployments at enterprise customers.The Role We are hiring a hands-on engineer to support the delivery of our computer vision / ML products into production. This role sits at the intersection of software engineering applied machine learning, with a strong focus on making ML models run fast, reliably, and at scale on edge devices. You will be responsible for our core video processing framework and deployment stack, working closely with senior ML engineers to ensure model inference performance, stability, monitoring, and field success. While you won’t be expected to design new ML algorithms or lead model training, you will be involved in diagnosing model issues in the field and improving real-world performance through optimization and iteration.This is a great fit for someone who enjoys real-world ML delivery: video streams, edge devices, inference performance, and production debugging.Key Responsibilities ML Model Runtime & Edge PerformanceMake ML models run efficiently on edge devices (latency, throughput, CPU/GPU utilization, memory constraints)Support inference optimization and troubleshooting (profiling, batching, pipeline tuning, runtime constraints)Investigate real-world model failures (data quality, camera placement, lighting, drift, edge-case behaviour) and work with ML engineers on mitigation strategiesEnsure robust model rollout processes: versioning, validation, safe deployment cyclesVideo Pipeline Engineering (Core Focus)Design and optimize real-time video processing pipelines using GStreamerIntegrate and manage streams from IP cameras (RTSP/ONVIF) and USB camerasDebug complex video stream issues (encoding/decoding, dropped frames, jitter, latency, network instability)Deployment & Production OperationsPackage and deploy services using Docker/Podman on Linux-based edge systemsTroubleshoot issues directly on production/staging Linux hosts (logs, profiling, system-level debugging)Implement and maintain monitoring and device health checks (e.g., Checkmk or similar)Event Streaming & InterfacesBuild interfaces between edge devices and online tools / connected machinesWork with event streaming systems (Kafka or similar) for detections, events, and telemetrydeep Kafka expertise isn’t required, but strong conceptual understanding isMust-Have Skills2–5 years of professional experience in software engineering / applied ML engineeringStrong Python skills (asyncio, threading, multiprocessing)Strong Linux skills: CLI, systemd, bash scripting, networking fundamentalsSolid experience with containerization (Docker or Podman)Comfortable debugging real systems remotely and working end-to-end (not just coding isolated modules)Interest in ML delivery and computer vision systems in productionNice to HaveExperience with GStreamer (big plus)Familiarity with computer vision pipelines (OpenCV, image processing)Experience with FFmpeg, RTSP, H.264/H.265, ONVIFWebRTC exposure (low-latency streaming)Kafka / message broker familiarityGerman language skills (corporate language is English)Why This Role is InterestingYou’ll work at the “real ML” layer: getting models running in production environments where conditions are messyStrong collaboration with senior ML engineers, with room to grow into more ML responsibility over timeDirect ownership of the edge inference video stack powering real customer deploymentsInternational team, low bureaucracy, hands-on culture

Location: Munich (Hybrid) Type: Full-time Company Overview We are working with a fast-growing Vision / AI software company building production-grade computer vision systems for the food and retail sector. Their products help customers reduce food waste, improve operational efficiency, and contribute to sustainability goals by enabling better decision-making through real-time visual intelligence. With an international footprint across Europe, the US, and Asia, they combine startup speed with real-world deployments at large enterprise customers. The Role We are looking for a Senior Machine Learning Engineer to take hands-on technical ownership of a key vision product that is moving into field testing with major retail partners in Germany. This role is ideal for someone who enjoys being deeply involved across the entire ML lifecycle - from model development and training through to deployment on edge devices at customer sites. You will act as a hands-on technical lead for the product, driving model improvements, performance validation, and production rollouts. Key Responsibilities Model DevelopmentDesign, implement, and iterate on deep learning architectures for real-time object tracking and event detectionTrain and optimize object detection models using production datasets and domain-specific video dataContinuously improve model robustness for real-world conditions (lighting changes, occlusions, camera angles, motion blur, etc.)Performance Evaluation & ValidationBuild and execute evaluation workflows for accuracy latency benchmarkingTest models using benchmark video datasets and dedicated hardware setupsMonitor model performance regressions and validate incremental updates before releaseDeployment & Integration (Edge / Production)Own the technical process of deploying model updates into production systemsEnsure stable integration of models into the wider software stack running on-siteSupport field testing cycles, troubleshooting and optimizing performance on edge devicesTooling & PipelinesMaintain and improve internal pipelines for:automated model trainingdata versioningperformance testingreproducible experimentationDrive best practices across model development and deployment workflowsRequirements5–8 years experience in Machine Learning / Deep Learning / Computer VisionStrong proficiency in Python PyTorchHands-on experience training object detection models (e.g., YOLO-style / Faster R-CNN / transformer-based detectors, etc.)Solid software engineering skills in a Linux environmentStrong ownership mindset: able to maintain and advance the full ML stack end-to-endMotivated to learn and apply new methods and improve production qualityMust HaveNative German speaker (customers and field partners are Germany-based)Nice to HaveExperience deploying ML models to edge devices / embedded environmentsFamiliarity with performance profiling / inference optimizationExperience with real-time video pipelines and production CV systemsWhat’s On OfferHybrid working model in MunichFlat hierarchies, high ownership, hands-on cultureInternational, multicultural environment with colleagues across multiple regionsDirect impact on a product entering real-world rollout with major German retailersBenefits/perks including mobility options, company events, and additional corporate benefits

Senior / Principal Research Scientist – Core AI Algorithms (Autonomous Systems)Location: Germany (Remote-first within Germany, on-site in Frankfurt every 2–4 weeks)About the RoleWe are partnering with a global automotive OEM building a core AI research and algorithm team responsible for the foundational intelligence behind next-generation automated driving systems.This role is research-driven and sits upstream of product teams. The focus is on inventing, validating, and transitioning new perception and world-modeling algorithms from research into production-ready systems. The team operates similarly to a big-tech research lab, but with a clear path to real-world deployment.Research Focus AreasDepending on background and interest, you may work on topics such as:3D scene understanding and world modelingOccupancy, motion forecasting, and dynamic scene reconstructionMulti-sensor perception (camera, LiDAR, radar)Representation learning for autonomous systems (BEV, implicit / generative 3D, Gaussian models, foundation models)Robustness, generalization, and long-tail perceptionLearning under weak, sparse, or noisy supervisionBridging offline training with real-world deployment constraintsKey ResponsibilitiesConduct original research in perception and autonomous systems with clear technical ownershipDesign and prototype novel algorithms and learning frameworksPublish at or contribute toward top-tier conferences and journals (e.g., CVPR, ICCV, ECCV, NeurIPS, ICRA, IROS)Translate research ideas into scalable, production-oriented implementationsCollaborate with applied ML, systems, and hardware teams to ensure feasibilityShape the long-term technical roadmap of the core AI organizationMentor junior researchers and engineers where appropriateRequired BackgroundPhD (or equivalent research experience) in Computer Vision, Machine Learning, Robotics, or a related fieldStrong publication record at top-tier conferences or journalsExperience conducting research within an industrial or applied settingExcellent understanding of modern deep learning methods and 3D perceptionStrong programming skills in Python and/or C Ability to work across the full spectrum from theory to implementationStrongly PreferredResearch experience in autonomous driving, robotics, or embodied AIWork on 3D perception, tracking, SLAM, or world modelsExperience at big-tech research labs, industrial AI labs, or advanced OEM R&DFamiliarity with real-world constraints such as runtime, memory, and system integrationPrior collaboration with product or engineering teamsWhat’s on OfferA research-first role with real influence on production systemsThe opportunity to define core algorithms, not just incremental improvementsA team culture that values publications, patents, and long-term thinkingRemote-first working model within Germany, with regular in-person collaboration in FrankfurtCompetitive compensation aligned with senior / principal research profilesWho This Role Is ForResearchers who want their work to ship into real vehiclesIndustry researchers seeking greater technical ownershipPhD-level candidates who enjoy both publishing and buildingProfiles combining academic depth with practical engineering maturityLooking forward to seeing your profile!

Location: Europe (strong preference for Spain, ideally Madrid) Type: Full-time About the Company We're working with a high-growth startup developing AI systems that allow industrial robots to perform tasks they currently cannot, starting with complex warehouse operations like mixed palletizing. Their technology combines deep reinforcement learning (DRL) with modern sequence modeling to tackle control and combinatorial optimization problems where classical approaches fail.They are a small, highly skilled team. Joining us means having direct impact, minimal bureaucracy, and ownership over core technology that will be deployed in real-world, high-throughput environments. Role Overview As the second hire in the DRL team, you will own the end-to-end reinforcement learning stack: from problem formulation to algorithm design, large-scale training, evaluation, and deployment. You will work closely with the technical leadership to translate cutting-edge DRL research into practical production throughput at operational sites. This role is highly autonomous, requiring a hands-on expert capable of leading experiments, troubleshooting complex issues, and establishing best practices for algorithm development and deployment. Key ResponsibilitiesDesign, implement, and ship DRL algorithms (e.g., PPO, SAC, DDQN and variants) incorporating advanced architectures such as encoders, cross-attention, and pointer networksOptimize stability and sample efficiency using techniques such as GAE, reward shaping, normalization, entropy/KL control, curriculum learning, and distributional/value-loss tuningSet up and manage large-scale training pipelines: multi-GPU training, parallel rollouts, efficient replay/storage, reproducible experimentsProductionize algorithms with clean, maintainable PyTorch code, profiling, Dockerized services, cloud deployments (AWS), experiment tracking, and dashboardsCollaborate with leadership to align technology with business goals and customer needsMentor and grow future team members, fostering a culture of technical excellence and innovationRequired QualificationsProven track record delivering DRL systems beyond academic demos: led at least one end-to-end DRL system from concept to production or achieved a state-of-the-art benchmark in the last 3–5 yearsDeep expertise in reinforcement learning and deep learning, with strong PyTorch skillsSolid understanding of DRL theory: MDPs, Bellman operators, policy gradients, trust-region/KL methods, λ-returns, stability and regularization in on-policy/off-policy regimesSystems experience: Python, Linux, multi-GPU training, Docker, cloud deployments (AWS preferred)Comfortable taking ownership of experiments, code quality, and results in a small, high-impact teamPhD or equivalent experience in DRL is acceptable; strong academic-only candidates considered if they demonstrate deep expertiseNice to HaveRobotics experience is not requiredProduction system deployment experience is beneficial but not mandatoryLocation & TravelEU-based (CET ±1) with occasional travel to customer sitesPreference for candidates in Spain; otherwise, EuropeCompetitive Compensation & Real Equity Offered.Interview ProcessDeep Technical Session – with CTO, focused on past DRL work (no coding tests, no homework)Traits & Skills Interviews – Two × 1-hour sessions with co-founders to assess problem-solving, communication, and startup fitTeam Meet & Offer – final discussion and reference checkWhy This Role is ExcitingWork at the frontier of DRL robotics in real-world, high-throughput industrial applicationsHigh autonomy, technical ownership, and direct impact on deployed AI systemsSmall, experienced founding team and strong early customer traction reduces commercial risk while maximizing technical challengeOpportunity to join a founding-stage team with equity and influence over core product and technology