LLM Performance Engineer Baden-WürttembergRemote with quarterly in person engineering workshops€110,000The work Most ML engineers never see what actually happens on the GPU. They train models, call an inference API, and trust the framework. If you have ever opened Nsight or Torch Profiler, followed a request through kernel launches and communication calls, and wondered why half the GPU time disappears into overhead, this work will feel very familiar. The problem Large language models behave very differently in production than they do in benchmarks. Token generation patterns change. Prefill and decode phases behave unpredictably. Communication overhead quietly kills throughput. Schedulers make decisions based on incomplete information. Most infrastructure platforms cannot see any of this.So they optimise the wrong things. Your work changes that. What you will actually build You will make the entire LLM execution path observable, from the moment a request hits the system to the moment CUDA kernels execute on the GPU. That means generating traces that capture:token-level model behaviourkernel launches and GPU utilisationruntime scheduling decisionsmemory movement and communication between GPUs You will use those traces to answer questions like: Why is a GPU only 55% utilised? Where does latency appear between prefill and decode? Why does a supposedly optimised attention kernel stall under load? Then you turn those answers into improvements. Better kernel behaviour. Better runtime execution. Better scheduling decisions across GPU fleets. The results show up in real numbers: higher GPU utilisation, lower latency and more throughput on production workloads. Why this work is different Most ML roles sit above the framework layer. This sits underneath it. You will spend your time inside PyTorch execution paths, CUDA behaviour, inference runtimes and distributed communication. The interesting problems live in the gaps between those layers. The systems you work on also run at meaningful scale. Clusters range from small internal deployments to environments with tens of thousands of GPUs. Performance improvements do not save milliseconds. They change how large fleets of hardware are used. The environment Small engineering team. Around sixty people. No layers of product managers translating problems for you. Engineers talk directly to each other and to the system. Work is fully remote, with occasional engineering sessions in Heidelberg focused on deep technical work rather than company rituals. Performance improvements are measured, validated and shipped to production systems used by paying customers.  You will likely enjoy this if You like profiling GPU workloads. You have dug into CUDA kernels, PyTorch internals or distributed training behaviour to understand why something performs poorly. You prefer investigating real systems over building ML features or training models. You care more about how models run than about how they are trained.

Senior Agentic AI Engineer$300,000 - $400,000Onsite, Palo Alto (Remote for exceptional talent)Full time / PermanentA well-known, frontier GenAI company is undergoing a major product pivot, moving from single-modal generative experiences toward a consumer multi-agent ecosystem designed to feel genuinely autonomous, useful, and alive.They’re building the core infrastructure that will define how millions of users interact with AI agents daily. From planning and execution to memory, creativity, and proactive behaviour. This role sits at the heart of this shift: designing and shipping the systems that make intelligent agents function for 1M users.What You’ll DoDesign and evolve the agent runtime, the core loop handling reasoning, tool use, planning, memory retrieval, and response generationBuild agent capabilities across modalities (e.g. image/video generation, voice interaction, browsing, code execution) and ship themOwn LLM orchestration and model routing across multiple providers, optimising latency, cost, reliability, and qualityImplement memory systems that allow agents to learn from interactions (long-term memory, episodic recall, semantic retrieval)Prototype and productionize autonomous behaviours such as proactive task execution, scheduling, and goal-directed workflowsCreate evaluation frameworks and metrics that measure agent quality, personality consistency, and real user impactWhat “Great” Looks LikeYou’ve personally built and shipped agentic systems, not just prompt wrappers or demosYou’re comfortable owning ambiguous, greenfield problems and turning ideas into working product fastYou think in systems: distributed workflows, multi-step reasoning, orchestration, reliabilityYou code daily and care deeply about performance, UX feel, and real-world usefulness(If you’re looking for a narrowly scoped role, heavy process, or pure research track, then this won’t be the right fit.)Why JoinJoin at a genuine product inflection point, early access launch, new architecture direction, and strong internal momentumWork in a small, elite engineering cohort where each senior hire has outsized ownership and influenceHelp define the company’s next-generation agent platform and model infrastructure from the ground upCollaborate closely with product leadership and shape how consumer AI agents evolve in the real worldClear trajectory toward technical leadership and founding-level impact as the organisation scalesIf you’ve built real agent systems and want to work on problems that don’t have playbooks yet, please apply with your resume!

AI Solution Architect – GenAI & Azure AI (Contract, Remote)We’re looking for a senior AI Solution Architect to lead the design of generative AI solutions built on Microsoft’s cloud and AI platforms. This role focuses on shaping end-to-end architectures for GenAI use cases and guiding delivery teams through to production.The role:You’ll own solution architecture across multiple generative AI initiatives, working from early use-case definition through to implementation. The focus is on designing scalable, secure, and production-ready AI solutions using Azure and Microsoft AI services.What you’ll be doingDesigning end-to-end architectures for generative AI and AI-driven applicationsTranslating business requirements into Azure-based solution designs and delivery approachesDefining patterns for LLM-enabled solutions, including search-augmented and retrieval-based architecturesMaking architectural decisions around Azure services, integration patterns, and deployment modelsProviding technical leadership to AI and engineering teams during deliveryReviewing solution designs and implementations to ensure quality, performance, and securityTechnical environmentMicrosoft Azure cloud services and PaaS componentsAzure AI and generative AI platforms (including LLM-based services and search-driven AI)Cloud-native architectures (serverless, containers, managed services)CI/CD pipelines and DevOps practices within Microsoft ecosystemsPython and/or modern Microsoft application stacksContract detailsInitial 9 month contract, (with strong potential to extend further)Fully remote€400–€425 per day

Role Overview We are seeking a Hardware Pre-Development Team Lead to guide the transformation of early-stage quantum sensing concepts into robust prototype systems ready for formal product development. You will work closely with physicists, hardware engineers, and software teams to define the technical roadmap, drive maturation of critical technologies, and implement agile workflows in experimental hardware environments. Key ResponsibilitiesLead a multidisciplinary hardware team advancing quantum sensing technologies from research prototypes to mature system architectures.Architect complex experimental platforms integrating optics, light sources, microwave electronics, magnetic systems, sensing elements, and precision mechanics.Define and execute structured workflows for prototype development, including iterative testing and verification processes.Apply agile methodologies (Scrum-inspired or equivalent) to enable fast iteration cycles, effective prioritization, and team collaboration.Collaborate with software and product development teams to ensure smooth transfer of validated subsystems into production.Mentor engineers and scientists, fostering a culture of ownership, technical excellence, and teamwork.Contribute to the long-term technical roadmap of our sensing platforms. Required QualificationsPhD or MSc in Physics, Electrical Engineering, Photonics, or related disciplines.7 years of experience in complex hardware or scientific instrumentation development, preferably in industry.Proven leadership of multidisciplinary engineering or R&D teams.Strong systems engineering mindset with experience integrating optics, electronics, sensing, and control systems.Track record of maturing experimental setups into robust prototypes or product-ready architectures.Experience applying agile methodologies in hardware or experimental development environments.Excellent leadership, communication, and team alignment skills.Comfortable operating in fast-paced, high-tech environments where research meets engineering.Preferred (Nice to Have)Experience in quantum sensing, advanced microscopy, semiconductor inspection systems, or precision measurement instrumentation. Why Join Us?Work on groundbreaking quantum technologies with real-world impact from day one.Lead a talented, international team passionate about solving hard problems.Shape the company culture, contribute to high-level decisions, and influence product strategy.Access learning and development resources, including courses, conferences, and workshops.Enjoy 30 vacation days, wellness memberships, and flexible working conditions.Participate in equity programs, directly sharing in the company’s success.

Engineering Manager Stockholm, Sweden73,000–93,000 SEK per month benefits Hybrid – 3 days office / 2 days remote Full-time Most ML leadership jobs pull you away from the models. This one puts you in charge of them. You will lead the generative audio systems that create music and sound effects for a global content platform used by millions of creators. The models already exist. The research direction is clear. What is needed now is someone who can own the entire system and push it into production at scale. You will guide how large diffusion models for music are trained, evaluated and deployed. Your decisions determine how these models evolve technically and how they run in real products where latency, stability and cost matter. What you will build You will help build systems that automatically adapt music to video, generate sound effects directly from visual input, and allow creators to produce soundtracks in seconds. A small team of five PhD educated ML engineers and a contractor will rely on your technical direction while you shape how the technology moves from experimentation into production. You will work across the full machine learning lifecycle. Training large generative models. Defining evaluation strategies. Making architectural decisions about inference, optimisation and deployment. Working closely with platform and MLOps engineers to ensure the systems run reliably in production.  Why this environment is different The models are trained on a proprietary catalogue of licensed music and structured datasets created through a global network of artists who produce and remix tracks specifically for training. This produces a dataset most AI labs simply do not have. You will also work close to the research frontier, with collaborations involving groups connected to unicorn start up labs and tier 1 universities.  The result is rare: frontier generative model work inside a stable, profitable company where the technology actually ships to users.  What you bringDeep experience training large machine learning models. Experience with generative models such as diffusion, audio models, vision models or large language models. Strong ML system design skills across training, evaluation and production deployment. Comfort guiding engineers and making architectural decisions that shape how ML systems evolve. Experience shipping ML systems where latency, reliability and cost matter. Team and setup You will lead a team of five PhD educated engineers and one contractor working on generative audio systems. The team works closely with platform engineering, data infrastructure and MLOps to ensure models move from experimentation into production features.  Curious? If you have trained large generative models before and want ownership of the entire system rather than a narrow piece of it, this will likely be interesting. Send a message / apply, and I can share more context.

AI Product Manager - GenAII’m currently working with an AI native company that recently raised €3M and is building a series of vertical AI agents designed to automate real operational workflows across multiple industries.They already have a strong engineering team and are now looking for a Product Manager who can sit very close to engineering and help drive the pace of building and shipping AI products.This is not a traditional roadmap focused PM role. The person coming in will be heavily involved in hands on product development around AI agents, working with engineers on prompting, prototyping, defining agent behaviour, and helping run evaluation workflows to improve model performance.They are looking for someone comfortable operating in an early stage AI environment, where product ideas move quickly from concept to prototype to shipped product. The PM will also spend time speaking with customers to understand workflows and help design how these AI agents can solve real operational problems.The company operates more like an AI lab, identifying opportunities for automation, building AI agents around them, and then taking the successful products to market.If you are currently building AI driven products or exploring agent based systems, it could be a genuinely interesting conversation.

A leading mobile ad platform is looking for a Data Science Manager to join its Programmatic Data Science team. This team builds algorithms that compete in real-time ad auctions, outsmarting industry giants and optimizing ad delivery across thousands of apps. The role combines leadership and hands-on work. As a manager you will grow and mentor a team of data scientists, guide technical strategy, and contribute directly to building machine learning solutions, including recommender systems and neural networks. Ideal candidates have 5 years in data science, 2 years leading teams, strong Python skills, and experience working with large-scale data (AWS, Kafka, Spark, Flink, S3, MySQL).Experience with MLOps is a plus.The role requires someone who can dive deep into technical challenges while communicating clearly across teams.Company offers a hybrid setup, flexible hours, relocation support to Hamburg, 30 vacation days, an in-house gym, mental health support, and regular team events. The office has central location and lake views, modern equipment, and a culture that values collaboration and celebrating success. This is an opportunity to lead a high-performing team, tackle cutting-edge challenges, and shape the future of mobile advertising.

Senior Research Engineer – Generative AIGermany - Remote first €80,000 – €100,000 2 year contract  This role sits inside a research-driven engineering team building real Generative AI systems that are meant to leave the lab and prove their value in the world.It is about building working GenAI agents, putting them in front of partners, stress testing them, improving them and demonstrating that they solve meaningful problems. The domains range from public safety and social services to finance. The common thread is impact. In the first six months, you would join an applied project where the goal is to prototype a GenAI agent and convince an external partner that it creates tangible value. You would work closely with a senior researcher, iterating quickly, shipping regular merge requests, refining features, spotting technical risks early and improving the system week by week. There is a strong emphasis on being able to explain what you built, both to technical peers and to non-technical stakeholders. The environment is intentionally exploratory. New models, new agent frameworks, new tooling. If something promising appears, you are encouraged to test it. The team meets in person every Tuesday in Heidelberg, but beyond that there is flexibility. English is the working language.You might be refining prompts and evaluation loops for LLM-based systems, experimenting with coding agents, shaping system architectures, or mapping out a lightweight roadmap for how a prototype could evolve into something commercial. You will be close to decision making, not buried in a narrow implementation silo.Who we're looking for:Working with LLMs or GenAI in practice since at least 2023, comfortable building in Python with proper version control.A Master’s or PhD in Computer Science, AI or a related field fits well.Industry experience matters more than labels.Experience with coding agents such as Cursor or Codex is particularly interesting, as is familiarity with modern GenAI libraries and lightweight MLOps tooling.Just as important is adaptability. The technology moves fast and so does the direction of applied projects. The interview process is technical but practical. There is an initial technical conversation focused on engineering and GenAI fundamentals, followed by a motivational discussion, and then an in-person day that includes collaborative coding using AI coding agents. The coding session focuses more on how you think and structure a solution than on perfect syntax. This is suited to someone who enjoys building at the edge of what is currently possible with Generative AI, but who also cares whether the result genuinely improves something for real users.If this sounds interesting, please apply here and a member of the team will be in touch.