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Machine Learning & Cloud Infra Engineer Munich (On-site) | Full time About the Company Our client is an early-stage AI company building generative models that learn the physics and geometry of the real world in 3D, rather than working from flat images. The work is aimed at use cases in robotics, AR/VR, gaming, and film. The team is small, and researchers and engineers work closely together on both the models and the systems that take them into products. About the Role This role owns the infrastructure behind the company’s training and evaluation of large diffusion-based generative models. That covers GPU clusters, storage, orchestration, and the path to model serving. It is hands-on and systems-focused. You will work directly with the researchers who use what you build, so priorities come from real training problems rather than a roadmap handed down to you. What You’ll DoOwn and evolve the ML and cloud infrastructure used to train and evaluate large modelsProvision, scale, and maintain multi-node, multi-GPU environments on cloud and/or on-prem, including scheduling, quotas, and capacity planningSupport distributed training (PyTorch DDP/FSDP, NCCL) and keep large runs stable, performant, and reproducibleBuild and optimize storage and networking for petabyte-scale datasetsDeploy workloads with Docker and Kubernetes, and manage infrastructure-as-code with TerraformSet up monitoring, logging, and alerting for cluster health, job performance, and cost, and define SLOs and incident response practicesManage secrets, IAM, and network security across research and productionWork with researchers to unblock training and support the move from research to served modelsWhat We’re Looking For3+ years in infrastructure, platform, or cloud engineeringHands-on GPU performance debugging (CUDA/NCCL concepts, utilization, network bottlenecks, profiling)Strong experience running AWS, GCP, or Azure, including networking, IAM, and cost managementProficiency with Docker, Kubernetes, and TerraformStrong scripting and automation skills in Python plus Bash or PowerShellFamiliarity with distributed training in PyTorch (DDP/FSDP or similar)Experience with observability tooling such as Prometheus/Grafana, OpenTelemetry, or ELKExperience building CI/CD for infrastructure and ML workflowsNice to HavePrior ML infrastructure experience supporting large-scale trainingExperience running on-prem GPU clusters alongside cloud capacityExperience with model evaluation and serving infrastructureExperience running on-call and incident responseWhy JoinOwnership. You will be responsible for the infrastructure behind a research program, not one corner of a larger platform.Scale. Multi-node GPU training and petabyte-scale datasets are part of the daily work.Proximity to research. You will work alongside the people who depend on your systems.Influence. In a small team, your views on tooling and architecture carry weight.What to Expect This is an on-site role in Munich. Expect to work across the stack, from cluster operations to developer tooling, and to share responsibility for reliability. If this sounds relevant, get in touch for a confidential conversation.

Machine Learning & Cloud Infra Engineer London (On-site) | Full time About the Company Our client is an early-stage AI company building generative models that learn the physics and geometry of the real world in 3D, rather than working from flat images. The work is aimed at use cases in robotics, AR/VR, gaming, and film. The team is small, and researchers and engineers work closely together on both the models and the systems that take them into products. About the Role This role owns the infrastructure behind the company’s training and evaluation of large diffusion-based generative models. That covers GPU clusters, storage, orchestration, and the path to model serving. It is hands-on and systems-focused. You will work directly with the researchers who use what you build, so priorities come from real training problems rather than a roadmap handed down to you. What You’ll DoOwn and evolve the ML and cloud infrastructure used to train and evaluate large modelsProvision, scale, and maintain multi-node, multi-GPU environments on cloud and/or on-prem, including scheduling, quotas, and capacity planningSupport distributed training (PyTorch DDP/FSDP, NCCL) and keep large runs stable, performant, and reproducibleBuild and optimize storage and networking for petabyte-scale datasetsDeploy workloads with Docker and Kubernetes, and manage infrastructure-as-code with TerraformSet up monitoring, logging, and alerting for cluster health, job performance, and cost, and define SLOs and incident response practicesManage secrets, IAM, and network security across research and productionWork with researchers to unblock training and support the move from research to served modelsWhat We’re Looking For3+ years in infrastructure, platform, or cloud engineeringHands-on GPU performance debugging (CUDA/NCCL concepts, utilization, network bottlenecks, profiling)Strong experience running AWS, GCP, or Azure, including networking, IAM, and cost managementProficiency with Docker, Kubernetes, and TerraformStrong scripting and automation skills in Python plus Bash or PowerShellFamiliarity with distributed training in PyTorch (DDP/FSDP or similar)Experience with observability tooling such as Prometheus/Grafana, OpenTelemetry, or ELKExperience building CI/CD for infrastructure and ML workflowsNice to HavePrior ML infrastructure experience supporting large-scale trainingExperience running on-prem GPU clusters alongside cloud capacityExperience with model evaluation and serving infrastructureExperience running on-call and incident responseWhy JoinOwnership. You will be responsible for the infrastructure behind a research program, not one corner of a larger platform.Scale. Multi-node GPU training and petabyte-scale datasets are part of the daily work.Proximity to research. You will work alongside the people who depend on your systems.Influence. In a small team, your views on tooling and architecture carry weight.What to Expect This is an on-site role in London. Expect to work across the stack, from cluster operations to developer tooling, and to share responsibility for reliability. If this sounds relevant, get in touch for a confidential conversation.

Senior Product Manager – GPU & AI InfrastructureGermany | Hybrid from Heidelberg You’ll own the product that decides where and when AI workloads run.This is a chance to take ownership of an AI Scheduling product at a growing AI infrastructure business. You’ll define the multi-year product vision, decide what gets built and work directly with engineers, infrastructure teams and enterprise customers to solve the challenges of running AI at scale.The product sits around some of the hardest practical problems in AI infrastructure: how jobs are queued, how expensive GPU resources are allocated, and how latency, reliability and performance are managed as workloads grow.You’ll have real ownership. Rather than managing a quarterly backlog, you’ll shape the longer-term direction of the product, turn customer problems into clear product decisions and work closely with leadership and engineering to get those decisions into production.You’ll also have direct customer exposure, giving you the opportunity to understand how organisations are actually using AI infrastructure and feed those insights back into the product roadmap.This could suit you if you’re already a Technical Product Manager working on cloud infrastructure, platforms, developer tooling or cluster management and want to take ownership of a product at the centre of the AI infrastructure market.You’ll need:5+ years of Product Management experience, including ownership of infrastructure, platform or developer tooling productsExperience with technologies such as Kubernetes, OpenStack, Slurm, VMware or similarA good understanding of cloud, compute infrastructure and how AI/ML workloads are runExperience creating longer-term product roadmaps and working closely with engineers and technical customersA technical background through education or hands-on experienceExperience in AI/ML infrastructure or MLOps would be particularly relevant.You’ll be based in Germany and able to work from the Heidelberg office on a hybrid basis, with up to 30% travel between Germany and the US.If you’re interested in owning a product that sits at the intersection of AI, infrastructure and resource management, I’d be happy to tell you more.

Senior Product Manager – GPU & AI InfrastructureGermany | Hybrid from Heidelberg You’ll own the product that decides where and when AI workloads run.This is a chance to take ownership of an AI Scheduling product at a growing AI infrastructure business. You’ll define the multi-year product vision, decide what gets built and work directly with engineers, infrastructure teams and enterprise customers to solve the challenges of running AI at scale.The product sits around some of the hardest practical problems in AI infrastructure: how jobs are queued, how expensive GPU resources are allocated, and how latency, reliability and performance are managed as workloads grow.You’ll have real ownership. Rather than managing a quarterly backlog, you’ll shape the longer-term direction of the product, turn customer problems into clear product decisions and work closely with leadership and engineering to get those decisions into production.You’ll also have direct customer exposure, giving you the opportunity to understand how organisations are actually using AI infrastructure and feed those insights back into the product roadmap.This could suit you if you’re already a Technical Product Manager working on cloud infrastructure, platforms, developer tooling or cluster management and want to take ownership of a product at the centre of the AI infrastructure market.You’ll need:5+ years of Product Management experience, including ownership of infrastructure, platform or developer tooling productsExperience with technologies such as Kubernetes, OpenStack, Slurm, VMware or similarA good understanding of cloud, compute infrastructure and how AI/ML workloads are runExperience creating longer-term product roadmaps and working closely with engineers and technical customersA technical background through education or hands-on experienceExperience in AI/ML infrastructure or MLOps would be particularly relevant.You’ll be based in Germany and able to work from the Heidelberg office on a hybrid basis, with up to 30% travel between Germany and the US.If you’re interested in owning a product that sits at the intersection of AI, infrastructure and resource management, I’d be happy to tell you more.

Senior Product Manager – GPU & AI Infrastructure San Francisco Bay Area, CA | Hybrid You’ll own the product that decides where and when AI workloads run.This is an opportunity to take ownership of an AI Scheduling product at a growing AI infrastructure business. You’ll define the multi-year product vision, decide what gets built and work closely with engineers, infrastructure teams and enterprise customers to solve the challenges of running AI at scale. The product focuses on some of the biggest challenges in AI infrastructure: how jobs are queued, how expensive GPU resources are allocated, and how performance, latency and reliability are managed as workloads grow. You’ll have real ownership of the roadmap, translating customer needs into clear product decisions and working closely with Engineering, Sales and leadership to bring those decisions to life.This could suit you if you’re already a Technical Product Manager working on cloud infrastructure, platforms, developer tooling or cluster management and want to take ownership of a product at the centre of the AI infrastructure market. You’ll need:5+ years of Product Management experience, including ownership of infrastructure, platform or developer tooling products.Experience with technologies such as Kubernetes, Slurm, OpenStack, VMware or similar.A good understanding of cloud, compute infrastructure and how AI/ML workloads run.Experience defining multi-year product roadmaps and working closely with engineers and technical customers.Strong communication skills and the ability to make product decisions in a technically complex environment.Experience in AI/ML infrastructure, GPU scheduling or MLOps would be particularly relevant.You’ll be based in the San Francisco Bay Area, working in a hybrid setup, ideally 1–3 days per week in the office, with some flexibility. The role also involves travel between the US and Germany, up to 30% as business needs require. If you’re interested in owning a product at the intersection of AI, infrastructure and resource management, I’d be happy to tell you more.

Senior Product Manager – GPU & AI InfrastructureGermany | Hybrid from Heidelberg | €90,000–€100,000 base + performance incentivesYou’ll own the product that decides where and when AI workloads run.This is a chance to take ownership of an AI Scheduling product at a growing AI infrastructure business. You’ll define the multi-year product vision, decide what gets built and work directly with engineers, infrastructure teams and enterprise customers to solve the challenges of running AI at scale.The product sits around some of the hardest practical problems in AI infrastructure: how jobs are queued, how expensive GPU resources are allocated, and how latency, reliability and performance are managed as workloads grow.You’ll have real ownership. Rather than managing a quarterly backlog, you’ll shape the longer-term direction of the product, turn customer problems into clear product decisions and work closely with leadership and engineering to get those decisions into production.You’ll also have direct customer exposure, giving you the opportunity to understand how organisations are actually using AI infrastructure and feed those insights back into the product roadmap.This could suit you if you’re already a Technical Product Manager working on cloud infrastructure, platforms, developer tooling or cluster management and want to take ownership of a product at the centre of the AI infrastructure market.You’ll need:5+ years of Product Management experience, including ownership of infrastructure, platform or developer tooling productsExperience with technologies such as Kubernetes, OpenStack, Slurm, VMware or similarA good understanding of cloud, compute infrastructure and how AI/ML workloads are runExperience creating longer-term product roadmaps and working closely with engineers and technical customersA technical background through education or hands-on experienceExperience in AI/ML infrastructure or MLOps would be particularly relevant.You’ll be based in Germany and able to work from the Heidelberg office on a hybrid basis, with up to 30% travel between Germany and the US.If you’re interested in owning a product that sits at the intersection of AI, infrastructure and resource management, I’d be happy to tell you more.

Staff Robot Learning Engineer | Germany | Full-time You’ve already trained and deployed learning-based policies on real robots. Now you can help bring the next generation of humanoid robotics into the real world. You’ll join an ambitious robotics company developing general-purpose humanoids, building robot-learning systems that work beyond the simulator. You’ll train and improve policies, develop data pipelines, run experiments on physical hardware and tackle the challenges of reliable sim-to-real transfer. This is a hands-on engineering role for someone who combines strong machine-learning fundamentals with practical robotics experience. You’ll work across imitation learning, reinforcement learning and emerging vision-language-action models, helping turn promising approaches into robust robotic capabilities. You’ll need:Minimum 2+ years professional experience training real robot policies using reinforcement learning, imitation learning or learning from demonstration.Proven experience deploying learned policies on physical robots.Strong Python and PyTorch skills, with good C++.Experience with robot data collection, simulation and sim-to-real transfer.A solid understanding of robot control, action representations and closed-loop policy execution.Experience with humanoid manipulation, teleoperation, diffusion policies, vision-language-action models or real-time action chunking would be a strong advantage. If you want to turn advances in robot learning into capable, reliable humanoid robots, let’s chat!

Your Role We are looking for a Quantum Design Engineer to help create and refine superconducting qubit architectures with built-in error resilience. The role focuses on maximizing coherence and fidelity while embedding error mitigation directly into circuit layouts and topology. You will take designs from simulation to fabrication and measurement, collaborating closely with fabrication and measurement teams to ensure theoretical advances translate into functional, scalable quantum devices. What You’ll DoDesign and simulate superconducting qubits and multi-qubit circuits, balancing high coherence with intrinsic error protection.Develop novel architectures that reduce reliance on external error correction through design-level solutions.Benchmark decoherence mechanisms and engineer circuit geometries to minimize loss, crosstalk, and noise sensitivity.Use electromagnetic and circuit simulation tools (e.g., HFSS, Sonnet, COMSOL, Qiskit Metal) alongside custom simulation workflows.Collaborate across disciplines to integrate design with fabrication and measurement protocols.Document and analyse results, using structured feedback loops to drive continuous improvement. Who You AreMSc or PhD in Physics, Electrical Engineering, or a related field.Expertise in superconducting qubits, circuit QED, or microwave quantum devices.Proficiency with electromagnetic and circuit simulation tools (HFSS, Sonnet, COMSOL, Qiskit Metal, or equivalent).Strong understanding of decoherence mechanisms and strategies to mitigate them through design.Experienced in connecting simulation results with experimental validation.Clear communicator and collaborator across design, fabrication, and measurement teams. What We OfferOpportunity to define and shape the architecture of next-generation superconducting quantum processors.Early-stage responsibility with direct influence on prototypes and roadmap.Collaborative, science-driven environment spanning design, fabrication, and experiment.Professional growth opportunities including mentoring, training, and leadership development.Competitive compensation and benefits, including relocation support