Job Title: ML Tech Lead – Multimodal AILocation: London / Remote Europe ConsideredCompensation: Competitive Base Salary Bonus Travel AllowanceAbout the RoleWe are seeking a hands-on ML Tech Lead to build and lead a brand-new team in a recently created, well-funded AI initiative. You’ll be responsible for shaping the direction of a cutting-edge platform for AI-driven video search and discovery, combining audio, video, and text data. This is a high-visibility role with the chance to impact creative teams and artists globally.Key ResponsibilitiesLead a multidisciplinary team of backend, frontend, and AI engineersArchitect and develop a multimodal AI search platform (video, audio, text)Design and build scalable content ingestion, indexing, and retrieval systemsIntegrate ML models into production search infrastructure (vector search, Elasticsearch/OpenSearch)Mentor engineers and foster a high-impact, collaborative team environmentDeliver robust, production-ready systems on modern cloud infrastructureWhat We’re Looking ForStrong experience in machine learning, especially multimodal modelsHands-on technical expertise in building large-scale search or recommendation systemsProficiency in cloud-based architectures and scalable production systemsLeadership experience: building, mentoring, and guiding engineering teamsPassion for music, media, or creative technology is a plusWhy This Role is ExcitingLead a newly created, high-impact initiative within a global entertainment leaderWork with massive audiovisual datasets and state-of-the-art AI technologyShape tools that directly support artists, creative teams, and content discoveryBe part of a well-funded, forward-thinking AI lab with long-term growth opportunities

Lead AI Engineer Location: United Kingdom, Remote or Hybrid Salary: Competitive, dependent on experience About the Company An established global technology platform specialising in digital publishing and print infrastructure is launching a new AI venture focused on transforming how printed content is created. The company is building an AI powered design editor that allows users to generate fully customised, print ready designs directly from natural language prompts. Instead of relying on templates, the platform generates original layouts, graphics, and structured outputs automatically. This is a newly formed AI venture backed by an existing global platform, offering the opportunity to build a product from the ground up while benefiting from the support and infrastructure of an established organisation. The Role The company is now hiring a Lead AI Engineer to design and build the backend systems that power the AI workflows behind the product. This role sits at the intersection of backend engineering and applied AI. You will be responsible for architecting scalable AI systems that translate user prompts into structured, production ready design outputs. You will work closely with product and engineering teams to develop the core AI infrastructure, ensuring the system is reliable, scalable, and capable of supporting high traffic production workloads. Key Responsibilities Design and build backend systems supporting AI driven design generation Develop and maintain LLM pipelines and agent based AI workflows Architect scalable APIs and services using Python frameworks Build infrastructure that supports high volume AI inference and real time workloads Collaborate with product teams to translate user prompts into structured outputs and workflows Ensure reliability, observability, and performance across AI systems Contribute to the technical direction of a new AI product from its earliest stages Required Experience Strong experience building production backend systems using Python Experience working with FastAPI, Pydantic, or similar modern Python frameworks Hands on experience building or deploying LLM based systems Experience with agent architectures, RAG systems, or LLM pipelines Strong understanding of scalable system design and distributed infrastructure Experience deploying machine learning or AI systems into production environments Desirable Experience Experience with generative AI systems or content generation workflows Experience with containerisation and cloud infrastructure Previous experience in startup or early stage product environments Why Join Opportunity to help build a new AI product from the ground up Backed by an established global technology platform Work on cutting edge generative AI systems applied to real world creative workflows Small, highly technical team with significant ownership and impact

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.

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.