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

Senior ML Engineer – Autonomous Driving (Mapless, AI-First) A well-funded European deep-tech company is building fully AI-driven, mapless autonomous driving technology in collaboration with leading OEMs and Tier 1 suppliers. We are hiring experienced ML engineers who want to move beyond incremental ADAS and work on large-scale, AI-native autonomy systems deployed directly on vehicles. What You’ll Work OnLearning-based scene understanding from raw multimodal sensor dataOnline road topology & lane connectivity extractionMultimodal transformers / graph neural networks for dynamic traffic modelingEnd-to-end perception → prediction → planning architecturesEnsuring geometric & temporal consistency in real-world drivingDeployment of production-grade ML models to embedded vehicle systemsThis is not simulation-only research. Models are trained at scale and validated directly on real vehicles. What We’re Looking ForStrong ML fundamentals (deep learning, transformers, large-scale training)Solid Python skills; C for production integrationExperience in one or more of:Autonomous drivingRobotics3D computer visionMultimodal learningSensor fusionLearning-based planningPhD is welcome but not required. Real-world deployment experience is highly valued. Why Join?Flat technical structure with real ownershipStrong compute infrastructureClose collaboration with major automotive partnersEquity / stock optionsOpportunity to shape next-generation autonomy from the ground upLocation: Germany (hybrid model available)

About the RoleWe are seeking a Senior Quantum Error Correction Scientist to develop fault-tolerant protocols and error correction schemes across diverse quantum hardware platforms. This is a rare opportunity to contribute to foundational research and help define scalable quantum architectures. Key Responsibilities:Design and evaluate quantum error correction schemes for heterogeneous hardware architecturesDevelop efficient fault-tolerance protocols under realistic noise and hardware constraintsBuild simulation and modeling tools for system analysis and designLead research projects and collaborate with multidisciplinary teams QualificationsPhD in Physics, Computer Science, Electrical Engineering, or related field5–10 years of experience in quantum error correctionStrong record of publications in QEC or related fieldsExperience with scientific computing and programmingProven ability to lead collaborative research and communicate resultsPreferred Skills:Translating research into hardware-aware protocols or commercial applicationsHands-on experience with quantum devices or simulationsKnowledge of decoding techniques and fault-tolerance architectures Benefits Visa Sponsorship: Open to supporting Visa to attract Top TalentCompensation: Competitive salary and equityHealth Coverage: Company-sponsored benefits for you and your familyWork at the cutting edge of quantum computing researchRegular offsites and social activities to recharge and connectShape scalable and fault-tolerant quantum systems for real-world applicationsFlexible and supportive work environment

AI Project Manager (Contract)We’re partnering with a leading Consulting organisation to hire an AI Project Manager to drive high-impact AI initiatives within a Microsoft/Azure environment. This is a fantastic opportunity to work at the forefront of AI delivery, collaborating with senior stakeholders and cutting-edge technical teams. The RoleOwn and deliver AI and data projects end-to-end within an Azure ecosystemAct as the key interface between business stakeholders and technical teamsDrive project planning, execution, and governance across multiple workstreamsEnsure clear communication, alignment, and successful delivery in a client-facing environmentWhat We’re Looking For5–8 years’ experience in project management within tech, data, or AI environmentsProven track record delivering AI / ML or advanced analytics projectsStrong experience with Microsoft technologies, particularly AzureConsulting or client-facing delivery experience is highly desirableExcellent stakeholder management and communication skillsFluent English is essentialAdditional InfoFully remote within Europe (Europe based in a must have)Fast-paced, high-visibility project environmentImmediate start preferred

Senior Data Engineer - HealthTech$150,000 - $200,000  Fully RemoteFull time / Permanent A fast-growing healthcare technology start up is looking for a Senior Data Engineer to join their team. They are actively working with large healthcare organizations across the U.S, helping them get more value from their data through AI-powered clinical tools. This work has a direct impact on how care is delivered and the data teams sit at the centre of that. This is a hands-on role. You will be building and owning data pipelines that feed real AI systems used by clinicians every day. Why JoinSmall, collaborative teams where senior engineers have real ownershipClear progression to director. The company is growing and promotes from withinCompetitive base salary, equity, flexible hours, and strong benefitsWork that has a tangible impact on patient care across the U.S.The Role You will join a small team responsible for integrating data from large healthcare organisations into a modern cloud data platform. Day to day, you will be building pipelines, validating data quality, and making sure the right data reaches the right systems reliably and on time. You will also contribute to shared tools and frameworks that make future integrations faster, work that scales well beyond your individual projects. What We're Looking For4 years of experience building production data pipelinesStrong SQL skills across large, complex datasetsProficiency in Python for data transformationExperience with cloud-based data platforms and distributed processing toolsComfortable working with healthcare data formats and standards, or willing to learn quicklyExperience with Azure cloud servicesUseful but not essential:Background in healthcare data or EHR systemsExperience with modern lakehouse architecturesExposure to real-time data pipelines or message-based data feedsExperience in a SaaS or multi-tenant environmentTech Stack Azure Data Factory - Databricks - Python - SQL - PySpark - CI/CD tooling - Healthcare data standards What Success Looks LikePipelines delivered on time, well tested, and clearly documentedData quality issues caught early — before they reach productionReusable components that speed up future workStrong working relationships with partner technical teamsNoteworthy - This is a full-time / permanent role and cannot be considered for C2C, C2H, or any other temporary contracts.

Senior Data Engineer - HealthTech$150,000 - $200,000  Hybrid - Houston, TX Full time / Permanent A fast-growing healthcare technology start up is looking for a Senior Data Engineer to join their team. They are actively working with large healthcare organizations across the U.S, helping them get more value from their data through AI-powered clinical tools. This work has a direct impact on how care is delivered — and the data teams sit at the centre of that. This is a hands-on role. You will be building and owning data pipelines that feed real AI systems used by clinicians every day. Why JoinSmall, collaborative teams where senior engineers have real ownershipClear progression to director. The company is growing and promotes from withinCompetitive base salary, equity, flexible hours, and strong benefitsWork that has a tangible impact on patient care across the U.S.The Role You will join a small team responsible for integrating data from large healthcare organisations into a modern cloud data platform. Day to day, you will be building pipelines, validating data quality, and making sure the right data reaches the right systems reliably and on time. You will also contribute to shared tools and frameworks that make future integrations faster, work that scales well beyond your individual projects. What We're Looking For4 years of experience building production data pipelinesStrong SQL skills across large, complex datasetsProficiency in Python for data transformationExperience with cloud-based data platforms and distributed processing toolsComfortable working with healthcare data formats and standards, or willing to learn quicklyExperience with Azure cloud servicesUseful but not essential:Background in healthcare data or EHR systemsExperience with modern lakehouse architecturesExposure to real-time data pipelines or message-based data feedsExperience in a SaaS or multi-tenant environmentTech Stack Azure Data Factory - Databricks - Python - SQL - PySpark - CI/CD tooling - Healthcare data standards What Success Looks LikePipelines delivered on time, well tested, and clearly documentedData quality issues caught early — before they reach productionReusable components that speed up future workStrong working relationships with partner technical teamsNoteworthy - This is a full-time / permanent role and cannot be considered for C2C, C2H, or any other temporary contracts.

We are seeking an experimental physicist with strong hands-on experience in atomic, optical, or quantum systems to help build and operate advanced experimental platforms. You will work directly with precision hardware for qubit control, measurement, and system scaling, contributing to the development of next-generation quantum technologies.This is a lab-focused role for someone who enjoys designing experiments, troubleshooting complex setups, and collaborating across disciplines to turn ideas into working systems. Responsibilities Design, build, and characterize optical, vacuum, and/or cryogenic experimental systemsImplement protocols for qubit preparation, control, and readoutIntegrate lasers, RF/microwave systems, control electronics, and data acquisitionAnalyze experimental data and optimize performance and stabilityTroubleshoot hardware and control issues across the full experimental stackCollaborate with engineers and scientists to inform system design and scalingRequirements Ph.D. in Physics, Applied Physics, Electrical Engineering, or related fieldHands-on experience with experimental quantum systems (AMO, solid-state, or superconducting)Familiarity with qubit control, spectroscopy, or precision measurementStrong experimental problem-solving skillsExperience using Python or similar tools for experiment control and analysisA collaborative mindset and clear communication skills

Simulation Engineer Location: Onsite - Bay Area.Company: High-growth AI startup (stealth / early-stage)Focus: Physics-based simulation to ML-driven systemsOverviewOur client is building a new class of AI systems designed to understand and operate within real-world physical environments. The company sits at the intersection of simulation, machine learning, and industrial systems, with a focus on turning high-fidelity simulation data into scalable, production-grade intelligence.They are hiring Simulation Engineers across multiple domains who can bring deep subject-matter expertise and translate complex physical systems into computational models that can be learned, optimised, and deployed. This is not a pure research role. It is for engineers who have built and used simulation systems in real-world environments and understand how those systems behave under production constraints.Key Areas of HiringCandidates should come from one of the following domains:Bioreactors / Bioengineering (top priority)CFD / Fluid Dynamics (medical devices or industrial systems)Aerospace (flight physics, aerodynamics, control systems)Fixed-Wing Drones / UAVsAviation (commercial or defence aircraft systems)Space / Rocket SystemsWhat You’ll DoDevelop and apply high-fidelity simulation models across fluid, structural, thermal, biological, or aerodynamic systemsTranslate simulation outputs into ML-compatible datasets and representationsWork closely with ML and AI teams to enable surrogate modelling, optimisation, and system-level learningImprove simulation performance, scalability, and reliability across large-scale compute environmentsDesign end-to-end pipelines from simulation through to data generation, model training, and deploymentValidate and calibrate models against real-world data where availableWhat They’re Looking ForCore Requirements:Strong background in simulation engineering within a real-world domainExperience with tools such as OpenFOAM, ANSYS Fluent, STAR-CCM , Abaqus, ANSYS Mechanical, COMSOLExperience building or working with custom simulation frameworks (C , Python, MATLAB or similar)Solid understanding of physics-based modelling (fluids, thermodynamics, structural mechanics, control systems, or bio-systems)Experience working with large-scale simulations or HPC environmentsPreferred:Exposure to ML workflows (PyTorch, TensorFlow, surrogate models, optimisation loops)Experience generating or working with synthetic data from simulationsFamiliarity with distributed compute, GPU acceleration, or cloud-based simulation pipelinesBackground in companies such as:Medical Devices: Stryker, Medtronic, Boston Scientific, Zimmer BiometDrones/UAVs: Skydio, DJI, Autel, ParrotAerospace/Aviation: Boeing, Airbus, Joby, defence organisationsSpace: SpaceX, Relativity Space, NASA, Project Kuiper, Muon SpaceWhat Makes This DifferentYou are helping turn simulation into intelligence, not just running modelsDirect exposure to next-generation AI systems grounded in physicsOpportunity to work across multiple industries and problem domainsHigh ownership in shaping how simulation integrates into AI systems for the physical worldIdeal ProfileDomain expert first, not a generalistHas built simulations that informed real-world decisionsComfortable operating in ambiguous, early-stage environmentsInterested in bridging physics and machine learningHiring PriorityBioreactors / Bio-simulation (urgent)CFD / Fluid systemsAerospace / UAVAviationSpace systems