Nathan Wills

AI Product Manager | 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.

Senior Systems Performance Engineer – Embedded TechnologyLocation: Amsterdam, NetherlandsWorking Pattern: Hybrid – 2 days per week in the officeSalary: €100,000 – €140,000 per annum + packageJob Type: PermanentThe OpportunityWe’re working with a global technology organisation developing next-generation systems and connected devices, and they’re looking for a Senior Systems Performance Engineer to join a specialist engineering and research team.This is a highly technical role focused on making complex devices faster, smoother and more power-efficient.You’ll work at the intersection of operating systems, system software and hardware, investigating difficult performance problems and developing solutions that can ultimately be implemented and measured on real devices.This isn't an AI model development role. The focus is on low-level systems performance, operating systems and device optimisation.What You'll Be Working OnDepending on your background, you could be working across areas including:Operating system and kernel performance optimisationCPU scheduling and resource allocationProcessor and core selectionPower and thermal managementDynamic Voltage and Frequency Scaling (DVFS)CPU, GPU and memory performanceSystem profiling and performance analysisApplication responsiveness and latencyGraphics, rendering and frame performanceRuntime and framework optimisationHardware/software performance optimisationPerformance improvements across heterogeneous computing environmentsThe team works on complex problems where improvements need to be measured, validated and demonstrated on real hardware.What You'll Be DoingInvestigating complex system-level performance bottlenecksProfiling systems to understand where processing time and resources are being consumedDesigning and implementing performance improvementsOptimising scheduling and resource allocationWorking with CPU, GPU, memory and other hardware resourcesBalancing performance, power consumption and thermal constraintsImproving application responsiveness and system smoothnessCollaborating closely with hardware, chipset, OS and software engineering teamsResearching new approaches to system performance and evaluating their practical valueTaking ideas from investigation and prototyping through to implementation and measurementContributing technical direction to future performance improvementsYour BackgroundWe're open to different technical backgrounds. You do not need to have experience across every area listed above.We're particularly interested in engineers with strong experience in one or more of the following:Linux Kernel / Android KernelOperating SystemsSystem SoftwareCPU SchedulingPerformance EngineeringPower ManagementThermal ManagementDVFSMemory PerformanceGPU / Graphics PerformanceAndroid FrameworksRuntime OptimisationEmbedded SystemsSoC / Chipset PerformanceRelevant titles could include:Senior, Staff or Principal Systems Engineer, Kernel Engineer, OS Engineer, System Software Engineer, Performance Engineer, Platform Engineer, Android Framework Engineer, Runtime Engineer or Graphics Engineer.Your current job title is less important than the depth and relevance of your technical experience.What We're Looking ForWe're particularly interested in engineers who can demonstrate that they have:Solved complex performance problems at system levelPersonally implemented technical improvementsUsed profiling or performance analysis tools to identify bottlenecksMeasured the impact of their work using metrics such as latency, frame rate, power consumption, memory usage or processing efficiencyWorked closely with hardware or chipset teamsDelivered changes that have been deployed to real devices or embedded platformsStrong programming experience, particularly in C/C++ or other low-level/system programming environmentsA typical strong candidate might come from an OS, kernel, Android, embedded, chipset or device-performance background.Why This Role?This is an opportunity to work on technically challenging problems where your work has a direct impact on real-world device performance.Rather than simply analysing performance issues, you'll have the opportunity to investigate the underlying cause, develop solutions and see those solutions implemented and measured on physical hardware.If you enjoy working close to the operating system, understanding how hardware and software interact, and solving problems that require genuine systems-level engineering, this could be a strong fit.Location & Practical DetailsAmsterdam, NetherlandsHybrid working, with approximately 2 days per week in the officeCandidates already based in the Netherlands or willing to relocate are encouraged to applyStrong spoken and written English requiredCompetitive salary in the region of €100,000–€140,000, depending on experience and overall packageCandidates with relevant notice periods are welcome to applyInterested?If your background is in systems, kernel, operating systems, embedded platforms or device performance, we'd be interested in hearing from you.

Senior Deep Learning Research ScientistDeepRec.ai  Berlin, Germany (Hybrid)Senior Research Scientist, Geometric Deep Learning and Scientific MLLocation: Berlin, GermanyWorking model: Full time, hybridAbout the companyWe are supporting an early stage deep learning company developing foundation models for engineering and physical systems.The company works with industrial partners across mechanical engineering, electrical engineering, manufacturing, and engineering design. Its goal is to create models that can generalise across different physical problems, geometries, and industrial constraints.This is a small research led team where new ideas can be implemented, tested, and improved quickly.The opportunityWe are looking for a Senior Research Scientist who can develop original deep learning methods for complex engineering problems.This is not a role focused on applying existing models without questioning them. You will be expected to understand why an architecture works, identify where current methods fail, and develop new approaches from first principles.You should have deep research expertise in at least one relevant area, while being willing to work across adjacent fields as projects develop.What you could work onDesigning and validating new geometric deep learning architecturesDeveloping models for graphs, meshes, point clouds, particles, surfaces, and other structured dataBuilding generative models for engineering design and physical systemsDeveloping surrogate models for computationally expensive simulationsTraining models using synthetic and simulated dataConditioning generative models on multimodal inputs and physical constraintsExploring neural operators, neural differential equations, diffusion models, and function space modellingStudying network architecture, optimiser behaviour, regularisation, loss geometry, and generalisationTranslating research papers into reliable experimental systemsWorking directly with industrial simulation environments and proprietary engineering datasetsWhat we are looking forA PhD in computer science, mathematics, applied mathematics, physics, engineering, or a related subjectResearch depth in geometric deep learning, scientific machine learning, generative modelling, synthetic data, surrogate modelling, neural operators, or a closely related areaStrong mathematical understanding of neural networks, optimisation, and generalisationEvidence that you can develop original methods rather than only reproduce existing researchExperience designing, training, and evaluating deep learning architecturesAbility to read papers critically and explain the reasoning behind technical decisionsStrong Python experience with PyTorch, JAX, TensorFlow, or similar frameworksInterest in applying research to difficult industrial problemsCandidates completing a strong PhD, experienced postdoctoral researchers, and researchers with several years of industry experience are all encouraged to apply.Useful additional experienceEquivariant neural networksGraph neural networksThree dimensional geometry, meshes, point clouds, or particle systemsPhysics informed learning and differentiable simulationSynthetic data generationReinforcement learning for optimisation or trajectory generationProcedural geometry generationSimulated data transfer into real applicationsExperience moving research models into production environmentsPrevious CAD, CAM, CNC, or manufacturing experience is not required.What is offeredDirect influence over the company’s research directionClose collaboration with the foundersFreedom to explore and test original ideasAccess to industrial datasets and simulation environmentsShort research and development cyclesConference attendance and continued learning supportFlexible working hoursPotential equity participationPossible relocation supportEnglish speaking working environment, with no German requirementThe team works from Berlin, with approximately two to three days per week available to work from home.If you enjoy developing new deep learning methods, reasoning from mathematical foundations, and applying research to physical engineering problems, we would be interested in speaking with you.