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Quantum Engineer We are looking for a Quantum Engineer to lead the development of next-generation quantum wiring products and enable scalable quantum technologies. This role combines hands-on lab work, software development, and experimental quantum research to turn advanced concepts into real-world prototypes.About the RoleYou will help build and operate our first quantum test lab, develop and optimize wiring solutions for quantum systems, and work closely with experimental and engineering teams to advance scalable quantum hardware. You will interact with researchers worldwide to inform product development, and contribute to building a high-performing, cross-disciplinary team.Key ResponsibilitiesQuantum wiring software: Develop algorithms, simulations, and feature specifications for wiring optimization and scalingProduct roadmap execution: Design and integrate components into demonstrators and scalable quantum products.Test suite development: Build mechanical, microwave, and quantum test systems to validate prototypes and products.Engineering collaboration: Provide guidance, feedback, and design specifications to RF, mechanical, and fabrication teams.Team building: Help attract and onboard top-tier quantum engineers to the team.Who You AreA hands-on problem-solver and builder, passionate about scaling quantum technologies. You enjoy tackling diverse challenges, from algorithm development to hands-on assembly, testing, and troubleshooting. You thrive in fast-paced, multidisciplinary environments and take ownership of complex technical problems.Key QualificationsPhD in experimental quantum computing or a closely related field (solid-state qubits, superconducting circuits, spins, dots, graphene, etc.).Experience specifying, installing, and operating complete cryogenic measurement setups, including DC and RF wiring.Strong Python skills for simulations, analysis, and optimization across quantum devices, RF systems, and thermal models.Deep understanding of passive microwave engineering and networks.Expertise in superconducting quantum devices, including qubit design, readout resonators, and single-shot measurements.Excellent communication and collaboration skills in English.Bonus SkillsExperience in qubit, PCB, or semiconductor fabrication techniques.Knowledge of heat transfer, cryogenics, RF connectors, soldering, or microwave engineering.Proficiency in French.Personal AttributesHands-on builder: comfortable creating setups, scripts, and prototypes from scratch.Meticulous, systematic, and engineering-focused problem-solver.Autonomous, ambitious, and organized; able to drive projects forward independently.Transparent communicator who values collaboration, constructive feedback, and teamwork.Eager to learn, grow, and push the limits of what is possible in quantum hardware.

Computer Vision Engineer - MLOps Location: Remote - (Must be EU based) Salary: Base salary equity We are hiring a Computer Vision Engineer to join a robotics company building intelligent, flexible robots for real manufacturing environments. This role is focused on production systems. You will be working on perception pipelines that run 24/7 on deployed robotic cells. The robots follow a modular architecture where new capabilities are continuously added, such as object recognition, grasp point estimation, anomaly detection, and task specific behaviours. As the system scales, the ML and MLOps foundations become critical. This role exists to own and extend those foundations. What you will be doingDesign, build, and deploy 2D and 3D computer vision systems used in live production environments. This includes image classification, object detection, semantic and instance segmentation, metric learning, and smart filtering.Take models end to end, from data and training through to deployment, optimisation, and monitoring.Contribute directly to an in house MLOps platform that supports data ingestion, experiment tracking, model versioning, deployment, and observability across multiple robotic capabilities.Work closely with robotics and hardware focused teams and help ensure models run efficiently and reliably on edge and production hardware. Over time, this includes model conversion and optimisation using tools such as ONNX and TensorRT. What we are looking for This is a production engineering role. We are looking for someone who has built ML systems before.Non-negotiable experience:Hands-on experience working with visual data in production systems (2D and/or 3D computer vision).Proven production ML experience: you have taken models from training through deployment and supported them in live environments.Strong Linux fundamentals, including working over SSH and operating production infrastructure.You have built MLOps systems, not just used them. This includes ownership of data pipelines, experiment tracking, model versioning, deployment, and monitoring.Solid understanding of how models actually work under the hood. You are comfortable reasoning about backpropagation, gradients, network architectures, and debugging model behaviour when things go wrong.Nice to have:Experience with robotics, autonomous systems, or other edge-deployed ML.Synthetic data generation and the ability to design efficient data collection strategies.Model conversion and optimisation workflows using ONNX and/or TensorRT.Experience with ROS, Kubernetes, and cloud platforms.Why apply?This role offers a rare combination of real-world impact and deep technical ownership. You are not optimising isolated models or working on disconnected experiments. You are helping define the perception and MLOps foundations for intelligent robotic systems that are already operating in production and will continue to scale over time. Engineers work in pods with clear ownership and the opportunity to grow into leading entire problem areas. Progression and compensation are tied to impact rather than tenure. The environment is fast-moving and flexible, with intense periods of work when needed, and a strong emphasis on transparency and alignment. This is a role for someone who wants to see their work move quickly from code to real machines on real factory floors.

Applied AI Engineer (End-to-End ML)Location: Palo Alto, CA (Hybrid)Role Type: Full-Time / PermanentOur client, a pioneering HealthTech AI company in Palo Alto, is seeking a high-calibre Applied AI Engineer to bridge the gap between advanced Machine Learning and robust Software Engineering. This is an end-to-end ownership role: you will be responsible for designing the logic, building the architecture, and deploying the final services. Core ResponsibilitiesArchitect AI Workflows: Design and implement sophisticated agentic workflows and automation sequences that power clinical decision-making.System Design & Integration: Build the backend infrastructure, scalable REST APIs, and data services required to support high-concurrency AI applications.Rapid Deployment: Maintain a high-velocity shipping cycle, moving from prototype to production-grade implementation in days.Model Orchestration: Select, fine-tune, and evaluate the performance of various LLMs (including OpenAI, Anthropic, and open-source models) for specific healthcare tasks.Full-Stack ML: Own the pipeline from data ingestion and time-series forecasting to real-time classification and model monitoring.Technical ProfileComputer Science Mastery: Expert knowledge of algorithms, data structures, and distributed systems.Software-Heavy Background: Professional-grade Python skills. You should be comfortable with software design patterns, testing, and CI/CD.Machine Learning Fundamentals: * Deep understanding of Core ML topics: classification, regression, and clustering.Specific experience in Time Series Forecasting and temporal data analysis.Proficiency in Generative AI: RAG architectures, prompt optimization, and agent frameworks.Infrastructure: Experience deploying services to cloud environments (GCP preferred) and a solid grasp of MLOps and pipeline automation.Education: BS in Computer Science or related field 4 years of experience, or an MS 2 years of experience.Cultural FitStartup Agility: You possess the "scrappiness" to solve problems with limited resources but the rigor to ensure those solutions are enterprise-grade.The "Generalist" Mindset: You enjoy working across the entire stack and are not afraid to dive into data engineering or infrastructure when needed.Mission-Oriented: You are motivated by the prospect of using AI to significantly improve healthcareOur client provides a highly competitive package, including a strong base salary, meaningful equity, and comprehensive premium healthcare benefits. You will join a world-class collaborative team in a hybrid environment in Palo Alto.Please apply for more details

Applied AI Engineer (End-to-End ML)Location: Palo Alto, CA (Hybrid)Role Type: Full-Time / PermanentOur client, a pioneering HealthTech AI company in Palo Alto, is seeking a high-calibre Applied AI Engineer to bridge the gap between advanced Machine Learning and robust Software Engineering. This is an end-to-end ownership role: you will be responsible for designing the logic, building the architecture, and deploying the final services. Core ResponsibilitiesArchitect AI Workflows: Design and implement sophisticated agentic workflows and automation sequences that power clinical decision-making.System Design & Integration: Build the backend infrastructure, scalable REST APIs, and data services required to support high-concurrency AI applications.Rapid Deployment: Maintain a high-velocity shipping cycle, moving from prototype to production-grade implementation in days.Model Orchestration: Select, fine-tune, and evaluate the performance of various LLMs (including OpenAI, Anthropic, and open-source models) for specific healthcare tasks.Full-Stack ML: Own the pipeline from data ingestion and time-series forecasting to real-time classification and model monitoring.Technical ProfileComputer Science Mastery: Expert knowledge of algorithms, data structures, and distributed systems.Software-Heavy Background: Professional-grade Python skills. You should be comfortable with software design patterns, testing, and CI/CD.Machine Learning Fundamentals: * Deep understanding of Core ML topics: classification, regression, and clustering.Specific experience in Time Series Forecasting and temporal data analysis.Proficiency in Generative AI: RAG architectures, prompt optimization, and agent frameworks.Infrastructure: Experience deploying services to cloud environments (GCP preferred) and a solid grasp of MLOps and pipeline automation.Education: BS in Computer Science or related field 4 years of experience, or an MS 2 years of experience.Cultural FitStartup Agility: You possess the "scrappiness" to solve problems with limited resources but the rigor to ensure those solutions are enterprise-grade.The "Generalist" Mindset: You enjoy working across the entire stack and are not afraid to dive into data engineering or infrastructure when needed.Mission-Oriented: You are motivated by the prospect of using AI to significantly improve healthcareOur client provides a highly competitive package, including a strong base salary, meaningful equity, and comprehensive premium healthcare benefits. You will join a world-class collaborative team in a hybrid environment in Palo Alto.Please apply for more details

Lead AI Engineer Fully Remote - Europe I am working with a European health tech scale up that is building production grade AI systems used by insurers, governments, hospitals, and pharma groups to support complex drug pricing and reimbursement decisions.They are hiring a Lead AI Engineer to own the AI architecture end to end and drive how LLM based systems are designed, evaluated, and shipped into production.The role is hands on and strategic. You would lead AI initiatives across RAG pipelines, agent workflows, and tool orchestration, while mentoring a small engineering team and working closely with product and platform leads. A big focus is on building systems that are observable, cost aware, and reliable, not demos.The environment suits someone who has spent time taking LLM systems from concept to real usage, has strong Python and backend experience, and understands tradeoffs around latency, throughput, and evaluation. Experience with LangChain or LangGraph, FastAPI style services, cloud infrastructure, and MLOps practices is important.The product domain sits at the intersection of healthcare, pricing, and access to medicines, with real world impact and complex constraints. The team is senior, international, and remote first across Europe.If this sounds aligned with the kind of problems you enjoy working on, feel free to share your updated resume/cv!

BMS & AI Edge Software Engineer Battery Systems | AI for Science | Energy Storage Our client is a publicly listed, AI driven energy technology company operating at the intersection of advanced materials science, battery engineering, and machine learning. Their mission is simple but ambitious: accelerate the global energy transition by using AI to fundamentally change how batteries are designed, validated, and operated. They are pioneers in applying AI directly to battery chemistry, materials discovery, and battery management systems, enabling next generation Li ion and Li metal batteries across transportation, energy storage, robotics, aviation, and defense adjacent applications. The Opportunity Our client’s Energy Storage Systems R&D group is seeking a BMS & AI Edge Software Engineer to design and deploy AI centric State of X (SoX) algorithms that run on edge devices. This role sits squarely between battery physics, embedded software, and applied machine learning. You will own algorithm development from concept through edge deployment, working closely with battery scientists, hardware engineers, and customer facing teams to bring production ready software into real world environments. Key Responsibilities Algorithm R&D for SoXDesign and implement SoX architectures covering charge, health, power, safety, degradation, and related metricsTranslate models and logic into production grade code running on edge devicesCollaborate with battery physicists and engineers on model selection and validationModel Design & OptimizationResearch and evaluate alternative algorithms to improve accuracy, robustness, and performanceOptimize models and software for real world operating constraintsPresent results internally and demonstrate measurable improvementsVerification & DeliveryTest and validate software as a production ready product using defined methodologiesSupport validation at customer sites or manufacturing plants as requiredEngage directly with customers to support deployment and technical approvalRequirements EducationPhD or Master’s in Electrical Engineering, Computer Science, AI, or a closely related fieldEquivalent hands on industry experience will be consideredExperience5 to 9 years of experience in Li ion batteries, BMS, or ESS software engineering (10 years for Senior level)Strong background in BMS sensing and control software including voltage, temperature, current, and diagnosticsSolid understanding of battery chemistries and characteristics such as OCV, C rate behavior, and impedanceExperience developing data driven or AI based algorithms for battery systems, ideally deployed on edge or cloudProven experience coding, integrating, validating, and delivering production softwareExposure to customer facing delivery or deployment projectsPreferred BackgroundBattery characterization methods such as GITT, dQ/dV, or similarPower electronics knowledge including DC/DC or DC/AC conversionFamiliarity with power delivery architectures such as UPS or battery backup systems for data centersWhat’s On OfferHighly competitive base salary and strong benefitsMeaningful equity participation in a publicly listed businessDirect impact on globally relevant energy and sustainability challengesWork alongside leading experts in AI, battery science, and engineeringLong term growth opportunities in a technically serious R&D environment

AI Engineer – Agent-Driven Development (Claude Code) Berlin (Hybrid, 3 days onsite) €120,000 base bonus We’re hiring an Applied AI Engineer to join a fast-growing AI consultancy in Berlin that focuses on shipping real production AI systems, not long advisory cycles or slide decks. Projects are short and intense, typically capped at three months, moving quickly from problem definition to prototype to production deployment inside real client environments. The setup is technical, fast, and built for engineers who like ownership and momentum. This role is built around Claude Code and agent-driven development. It is not a traditional AI or software engineering role. The core of the job is designing outcomes, writing specs and evals, orchestrating agents, and shipping systems faster than classical teams can. What you’ll be doingUsing Claude Code as your primary development interface, coordinating multiple agents in parallelDesigning specs, evals, and feedback loops rather than hand-coding implementationsTurning fast prototypes into enterprise-grade AI applications deployed to cloud environmentsIntegrating AI systems into real client platforms and production infrastructureWorking closely with senior technical leadership to define how agent-driven development should work in practiceShipping real systems for real organisations across industry, public sector, and NGOsWhy this is a good place to workStrong bias toward building and shipping real systemsEngineers are trusted to make decisions rather than follow rigid playbooksHigh technical bar with a pragmatic, low-ego teamExposure to a wide range of real-world problems rather than a single productThis role is a strong fit if youActively use Claude Code and have strong opinions on how to work with it effectivelySee reading every line of code as a bottleneck, not a virtueHave built evals and let agents run for extended periods to reach better outcomesHave converted APIs into MCP servers or understand why you wouldAre comfortable deciding when Claude Code beats alternatives like CodexPrefer speed, ambiguity, and responsibility over processThis role is not a fit if youWant to personally write most of the codeAre looking for a traditional AI or ML engineering roleNeed clearly defined tasks and guardrails before startingAre uncomfortable moving fast in uncertaintyWhat we’re looking for3 years building and shipping production software systemsStrong Python foundation and broad software engineering judgementExperience deploying and operating ML-backed systems in productionExperience with at least one major cloud platform, ideally AzureComfort shaping solutions where the problem is not fully specifiedGerman language ability at B2 level or higherWhat’s on offerUp to €120,000 base salary plus bonusHybrid setup with 3 days per week in BerlinA genuinely non-traditional engineering role centred on Claude CodeHigh autonomy and direct influence on how the team builds softwareIf you already work this way and feel most roles haven’t caught up yet, this one will make immediate sense.

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