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Multimodal AI Systems: Chief Scientist/ Principal Technical Leader We are seeking a senior leader to define and deliver the architecture and research direction for large-scale multimodal AI systems. This role combines scientific leadership with hands-on system ownership, spanning model innovation, training, inference, and production deployment. You will lead the design of multimodal architectures across LLMs, VLMs, video models, and multimodal agents, while driving cutting-edge research in multimodal understanding and generation. The role owns the full lifecycle from novel algorithms and publications to scalable, optimized systems (autotuning, quantization, inference efficiency). RequirementsDeep expertise in multimodal learning with hands-on experience training large-scale vision-language, video, or multimodal models.Strong understanding of transformers, diffusion models, and large multimodal model inference.Proven research impact (top-tier conferences preferred) and/or significant open-source contributions.Ability to translate frontier research into production-grade AI systems.

LLM Algorithm Lead$200,000 - $300,000San Francisco, HybridFull-time / PermanentA product-focused AI start-up is building LLM systems that run in production and are used daily by over a million professionals. This role is responsible for designing, shipping, and maintaining applied LLM systems that support real product features, with an emphasis on reliability, cost, and scale rather than experimentation. Why This Role MattersOwn how LLM systems behave in a large, user-facing productMake architectural decisions that affect reliability, latency, and costMove LLM features from prototype to stable production systemsSet technical direction for applied LLM algorithms and evaluation practicesWhat You’ll DoDesign structured LLM workflows, including planning, reasoning, and multi-step executionBuild and maintain core components such as memory, personalization, and reusable LLM modulesLead development of LLM-powered product features from design through productionBuild and optimize retrieval pipelines (RAG) via chunking, indexing, reranking, and evaluationSelect and route between models based on performance, cost, and latency constraintsDefine evaluation metrics, monitoring, and feedback loopsDebug production issues and drive algorithm-level improvementsWhat You BringExperience shipping LLM-based systems into productionStrong understanding of prompting, reasoning workflows, and system designHands-on experience with RAG systemsExperience building evaluation, monitoring, or safety mechanismsAbility to lead technical decisions and guide other engineersExperience with inference optimization, efficiency, or large-scale systems is a plus

AI Applied Engineer$200,000 - $300,000San Francisco, HybridPermanent / Full-timeA product-led AI start-up is building one of the most widely adopted AI work companions in the world, operating at massive real-user scale with millions of professionals relying on it daily. The challenge problem now is designing AI systems that reliably support complex knowledge work across preparation, collaboration, and follow-through, inside products people trust. This role is ideal for someone who wants to work across AI engineering, product thinking, and ultimately shape how AI actually shows up in day-to-day professional workflows. Why This Role MattersOwn how AI supports high-stakes knowledge workDesign multi-step AI workflows that users rely on repeatedlyHelp define how agent-like systems behave inside a consumer-grade productWork beyond prompt design into evaluation, iteration, and reliabilityWhat You’ll DoOwn the end-to-end design of AI-first workflows for preparation, collaboration, and follow-up Design and iterate multi-step LLM / agentic systems, spanning intent understanding, planning, tool invocation, memory usage, and refinement loopsBuild reusable AI skills, prompts, templates, and evaluation pipelines that can power multiple product experiencesDefine success metrics for AI behaviour, run experiments, and use real interaction data to improve usefulness and reliabilityPartner closely with engineering and ML teams to ship quickly while maintaining a high bar for product quality and user experienceWhat You BringProven experience shipping AI/ML powered products end to endStrong working understanding of LLM systems: prompting, tool calling, retrieval, context construction, evaluation, and common failure modesAbility to translate user needs into clear flows, specs, and examples, including edge cases and expected behavioursComfort working directly with data and interaction logs to debug issues and compare variantsHands-on experience designing agent-like workflows involving multi-step plans, multiple tools, and refinement or self-correction

Agentic AI Engineer$200,000 - $300,000San Francisco, HybridPermanent / Full-timeA product-led AI start-up is building one of the most widely adopted AI work companions in the world, operating at massive real-user scale with millions of daily interactions. The challenge has shifted to designing agent systems that can plan, reason, evaluate themselves, and operate reliably inside real products. This is an opportunity to work from first principles on agentic architectures that power production systems used by professionals globally. Why This Role MattersBuild agent systems that plan, act, reflect, and improve across complex, ambiguous user workflowsDefine foundational patterns for LLM tool-use, reasoning graphs, and self-evaluation in productionJoin at a point where agent architecture decisions will shape the long-term platformWork on problems beyond prompt engineering like runtime reliability, context limits, and learning flywheelsWhat You’ll DoDesign and implement Plan–Act–Reflection style agent architecturesBuild DAG-based reasoning flows to deconstruct user intent into executable stepsDevelop agent skills including function calling, MCP-style integrations, and streaming APIsSolve runtime problems like context overflow / context rot through isolation, compression, and offloading strategiesArchitect automated evaluation and learning pipelines (reward functions, LLM-as-judge, RFT-style systems)What You BringProven experience building and shipping agentic AI systemsStrong understanding of workflow design, failure modes, and deterministic executionComfort designing distributed systems, APIs, and protocols used across teamsPractical experience with agent orchestration frameworks

Machine Learning EngineerLocation: Zurich / RemoteLanguage: German/Swiss German Preferred (English Fluent/Professional) This is a priority search for a small investment group building an internal AI Lab across companies they actively own.The company operates across retail and FMCG supply chains. Their portfolio supports large offline and online retailers, with heavy operational workflows across trading, pricing, ERP, and CRM. Today a lot of value is lost to manual processes and fragmented systems.This hire will be the first dedicated technical builder in the AI Lab. The focus is hands on delivery, not research.The expectation is to design, build, and ship AI driven systems that improve trader productivity, reduce operational friction, and surface revenue opportunities. This includes agent based workflows, internal tools, and hands on AI and ML implementation.You would work closely with founders and operators, move quickly, and have real autonomy. There is also exposure to reviewing the technical architecture of new investments and shaping build decisions early.Small, elite team distributed across Europe. High responsibility and clear ownership from day one. Real upside tied to equity backed projects.Ideally Switzerland based. German or Swiss German is a strong plus, English is also required.If you enjoy building in ambiguous environments and want your work in production immediately, feel free to send your CV.

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