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Job Title: Humanoid Robotics Product Manager (Mandarin Speaking) 📍 Location: Dubai, UAE (Relocation Required) 💰 Salary: $5,000 – $10,000 per month (tax-free) A global industrial technology organisation is building a new robotics and automation division in Dubai, focused on deploying humanoid robots in real industrial environments such as warehouses, factories, construction sites, and maintenance operations. We are looking for a Humanoid Robotics Product Manager to lead the development and deployment of these systems — translating cutting-edge robotics capabilities into scalable commercial products. This role sits at the intersection of robotics engineering, product strategy, and industrial operations, working closely with robotics engineers, AI teams, and enterprise customers.Fluent Mandarin Chinese and English are mandatory for this role. Key Responsibilities • Define the product strategy and roadmap for humanoid robotics solutions in industrial environments • Identify high-value use cases across logistics, manufacturing, construction, and inspection • Translate real-world operational challenges into product requirements • Work closely with robotics hardware, AI/software, and autonomy teams • Lead product development from concept → pilot → large-scale deployment • Manage industrial pilots and convert them into scalable commercial offerings • Support go-to-market strategy, customer engagements, and strategic enterprise deals Requirements • Fluent Mandarin Chinese and English • 5 years of experience in product management or technical leadership in robotics / automation • Strong understanding of industrial environments (manufacturing, logistics, construction, etc.) • Experience delivering hardware software products from concept to deployment • Degree in Robotics, Engineering, Computer Science, or related field • Willingness to relocate to Dubai and travel internationally when required Nice to Have • Experience with humanoid robots or mobile manipulation systems • Familiarity with robot autonomy, perception, and control architectures • Experience launching robotics products into industrial markets 🚀 Why this role? • Work on cutting-edge humanoid robotics deployments • Join a fast-growing robotics initiative within a major global industrial group • Tax-free salary in Dubai • Opportunity to build products deployed across global industrial markets

Job Title: Humanoid Robotics Product Manager (Mandarin Speaking) 📍 Location: Dubai, UAE (Relocation Required) 💰 Salary: $5,000 – $10,000 per month (tax-free) A global industrial technology organisation is building a new robotics and automation division in Dubai, focused on deploying humanoid robots in real industrial environments such as warehouses, factories, construction sites, and maintenance operations. We are looking for a Humanoid Robotics Product Manager to lead the development and deployment of these systems — translating cutting-edge robotics capabilities into scalable commercial products. This role sits at the intersection of robotics engineering, product strategy, and industrial operations, working closely with robotics engineers, AI teams, and enterprise customers.Fluent Mandarin Chinese and English are mandatory for this role. Key Responsibilities • Define the product strategy and roadmap for humanoid robotics solutions in industrial environments • Identify high-value use cases across logistics, manufacturing, construction, and inspection • Translate real-world operational challenges into product requirements • Work closely with robotics hardware, AI/software, and autonomy teams • Lead product development from concept → pilot → large-scale deployment • Manage industrial pilots and convert them into scalable commercial offerings • Support go-to-market strategy, customer engagements, and strategic enterprise deals Requirements • Fluent Mandarin Chinese and English • 5 years of experience in product management or technical leadership in robotics / automation • Strong understanding of industrial environments (manufacturing, logistics, construction, etc.) • Experience delivering hardware software products from concept to deployment • Degree in Robotics, Engineering, Computer Science, or related field • Willingness to relocate to Dubai and travel internationally when required Nice to Have • Experience with humanoid robots or mobile manipulation systems • Familiarity with robot autonomy, perception, and control architectures • Experience launching robotics products into industrial markets 🚀 Why this role? • Work on cutting-edge humanoid robotics deployments • Join a fast-growing robotics initiative within a major global industrial group • Tax-free salary in Dubai • Opportunity to build products deployed across global industrial markets

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.

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

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.

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.

Senior ML Engineer – Autonomous Driving (Mapless, AI-First) A well-funded European deep-tech company is building fully AI-driven, mapless autonomous driving technology in collaboration with leading OEMs and Tier 1 suppliers. We are hiring experienced ML engineers who want to move beyond incremental ADAS and work on large-scale, AI-native autonomy systems deployed directly on vehicles. What You’ll Work OnLearning-based scene understanding from raw multimodal sensor dataOnline road topology & lane connectivity extractionMultimodal transformers / graph neural networks for dynamic traffic modelingEnd-to-end perception → prediction → planning architecturesEnsuring geometric & temporal consistency in real-world drivingDeployment of production-grade ML models to embedded vehicle systemsThis is not simulation-only research. Models are trained at scale and validated directly on real vehicles. What We’re Looking ForStrong ML fundamentals (deep learning, transformers, large-scale training)Solid Python skills; C for production integrationExperience in one or more of:Autonomous drivingRobotics3D computer visionMultimodal learningSensor fusionLearning-based planningPhD is welcome but not required. Real-world deployment experience is highly valued. Why Join?Flat technical structure with real ownershipStrong compute infrastructureClose collaboration with major automotive partnersEquity / stock optionsOpportunity to shape next-generation autonomy from the ground upLocation: Germany (hybrid model available)