Research

Multimodal LLM Researcher$300,000 - $400,000 Remote, Palo AltoFull-time / PermanentDeepRec has partnered with a high-growth generative AI company (Series B, $130M raised). They're building multimodal, multi-agent systems that combine language, vision, audio, and video. If you've been looking for a role where your research reaches production and shapes how millions interact with creative AI, this is worth a closer look.You'll help define the next generation of multimodal AI systems. Your work will span research, experimentation, and deployment, with a focus on real-time performance, multimodal reasoning, and agent-based workflows. You'll have the freedom to explore ambitious ideas while working alongside engineers who can bring them into production.What You'll Do- Lead research across LLMs, VLMs, and Audio Language Models- Design novel multimodal model architectures and training approaches- Improve real-time inference across text, image, audio, and video- Train and fine-tune autoregressive and diffusion models- Build and curate high-quality multimodal datasets- Collaborate with engineering teams to deploy research outcomes- Publish findings at leading AI conferences and journalsWhat You'll BringEssential- Strong research track record in multimodal AI or foundation models- First-author publications at recognised ML, vision, or audio conferences- Deep expertise in LLMs, VLMs, Audio LMs, or related fields- Strong Python and deep learning experience using modern frameworksDesirable- Experience with diffusion models or world models- Background in real-time AI systems and model serving- Experience building large-scale multimodal datasetsWe encourage you to apply even if you don't meet every requirement. The right mindset matters as much as the right CV.What's In It For You- USD 300,000–400,000 salary- Fully remote working arrangement- Ownership of research that shapes production systems- Opportunity to publish and contribute to the field- Direct collaboration with product and engineering leadershipThis role offers the chance to work on multimodal AI problems that sit at the intersection of research and real-world deployment. If you're excited by advancing the field while seeing your work reach users, we'd love to hear from you.

Chief Executive Officer – Deep-Tech Photonics Scale-UpCompany OverviewA well-funded deep-tech company with a strong scientific and technological foundation is entering a new phase of growth, transitioning from an R&D-led organisation into a commercially focused scale-up.The business operates at the frontier of advanced photonics and semiconductor technologies, with applications in next-generation computing and communications. Having established strong technical credibility and IP, the organisation is now focused on building the structures, leadership, and commercial engine required to scale globally. Role OverviewWe are seeking a Chief Executive Officer to lead the company through its next stage of growth and transformation.This role will be responsible for driving the professionalisation of the organisation, defining long-term strategy, and building a scalable commercial business while maintaining the company’s strong technical and entrepreneurial DNA.The CEO will work closely with the founders, board, and leadership team to translate deep-tech innovation into a sustainable, high-growth business. Key ResponsibilitiesStrategy & CultureDefine and execute long-term vision, strategy, and roadmapPosition the company to stay closely connected to emerging opportunities across markets, technologies, and partnersLead the transition from R&D-focused organisation to a commercially driven businessBuild a strong, values-led culture focused on innovation, excellence, and collaborationFundraising & GovernanceLead fundraising across venture capital, strategic investors, and public funding sourcesManage investor relations and board engagementEnsure strong governance, financial planning, and risk managementExecutive LeadershipBuild and lead a high-performing leadership teamOversee operational scaling in partnership with the COOAlign technical, commercial, and operational functions into a unified execution model PartnershipsDevelop and manage strategic partnerships across suppliers, foundries, and ecosystem partnersStrengthen positioning within key industry and research ecosystems Key Requirements ExperienceProven CEO, founder, or senior executive experience in deep-tech, semiconductor, photonics, or advanced hardware environmentsStrong track record scaling technology companies from early stage through commercialisationDemonstrated experience in fundraising and investor relationsMindsetStrong strategic thinker with hands-on execution capabilityAbility to bridge advanced engineering / physics with commercial realitiesHigh credibility with both technical teams and external stakeholdersComfortable operating in ambiguity and building structure in fast-evolving environmentsDegree in Engineering, Physics, Finance, Business, or related field preferredPersonal QualitiesEntrepreneurial, resilient, and mission-drivenStrong communicator with executive presenceValues integrity, transparency, and long-term impactWillingness to be regularly onsite and travel 20% of time Why This RoleLead a company at the forefront of next-generation photonic technologiesWork closely with a world-class technical founding teamTake ownership of a pivotal transition from R&D to global commercial scale-upHigh-impact leadership role with significant strategic influence and equity participation

AI Researcher – Video World Generation San Francisco (Bay Area)Help build the next generation of AI video systems that can create rich, interactive worlds from text or images.What you’ll work on:Foundational diffusion models and world models for high-quality video generationReal-time AI pipelines that turn ideas into consistent, dynamic video scenesMulti-agent systems and orchestration for intelligent video creationRL techniques for more adaptive and open-ended video generationRequired:Strong production experience with large multimodal or agentic AI systemsHands-on work with distributed training or large-scale vision/diffusion pipelinesBonus (big plus):Experience training or fine-tuning diffusion modelsBackground in world models, simulation, robotics, or multi-agent systemsFamiliarity with game engines (Unity/Unreal) as test environments

Our client is building the world’s first foundation model for physical infrastructure: electricity, gas, heat, and water. The networks that power modern civilization have run for decades on 1970s control logic, under-observed, under-optimized, and increasingly unequal to the demands of the energy transition. Our client was founded to change that. They have already built a composite, physics-grounded causal world model: six co-trained inference engines spanning physics-informed GNNs, causal time-series, topology discovery, federated training, edge inference, and techno-economic optimization. It is in production at 30 distribution system operators across 14 countries, covering 22 million connection points. Several of the world’s largest industrial automation and grid-technology vendors integrate it into their platforms, and it runs on the live grids of multiple tier-1 European utilities today. They are now building the next layer: a large-scale pre-trained foundation model for flow networks, trained on tens of billions of physics-consistent network states and governed by hard conservation-law constraints that no language model will ever learn from tokens. This is not applied AI. It is a new model class. DeepRec.ai is partnering with the company to assemble a small, exceptional team to build it. The data is real, exclusive, and unglamorous. The physics is non-negotiable. The impact is continental.   The problem you will work on Distribution grids are among the most complex dynamical systems on Earth: millions of nodes, time-varying topology, hard physical constraints, and almost no labeled ground truth. The state of the art is classical SCADA with a thin ML veneer. Our client is replacing it with a large-scale pre-trained foundation model, trained on synthetic and real network states, governed by Kirchhoff constraints as a hard loss term, and fine-tuned on operator-specific topologies via federated learning. Stage 1 pre-training target: 10¹? Newton-Raphson power-flow solutions across 50,000 distribution topologies. Stage 2: cross-network generalization to gas, heat, and water flow networks. Same architecture, different conservation laws. What you will doOwn end-to-end pre-training of the physics-informed GNN foundation model: data pipeline design, masked pre-training objective, distributed training infrastructure, and evaluation harness.Characterize scaling laws for physics-informed pre-training: data efficiency vs. compute trade-offs, emergence of physical consistency, and OOD generalization across unseen topologies.Design the pre-training corpus: synthetic topology generation, power-flow simulation at scale, and augmentation strategies that preserve physical validity.Lead the foundation-model preprint: own the architecture and pre-training sections, targeting a top-tier venue (NeurIPS, ICLR, ICML) or arXiv first.Interface with the causal world-model team on physics-informed loss formulation, and with the federated training team on privacy-preserving pre-training across operator estates.Represent our client externally at frontier AI venues. We expect this person to be a recognizable scientific voice for the model class being defined.Required profilePhD in machine learning, computer science, or computational physics from a leading research institution (e.g. ETH Zurich, Cambridge, Oxford, TU Munich, EPFL, UCL, ENS, or equivalent).3 to 6 years of post-PhD experience at a frontier AI lab or leading academic group (e.g. DeepMind, Meta FAIR, Mistral, EleutherAI, Stability AI, Kyutai, Aleph Alpha, Max Planck MIS, IDSIA, ELLIS-network member labs, or equivalent).First-author publications at NeurIPS, ICLR, or ICML on large-scale pre-training, masked modeling, GNN expressivity or scaling, or physics-informed deep learning.Hands-on experience training models at >1B parameter scale with distributed GPU/TPU infrastructure (PyTorch DDP/FSDP, JAX, or equivalent).Desirable: prior work at the intersection of graph neural networks and physical simulations, including molecular dynamics, fluid dynamics, power systems, or any PDE-governed network system.Desirable: experience with physics-informed neural networks (PINNs), neural operators (FNO, DeepONet), or Hamiltonian / Lagrangian networks.What our client offersA genuinely unsolved research problem at the intersection of physics, ML, and critical infrastructure, with exclusive access to real production data from 30 grid operators.First-principles technical latitude: you define the pre-training objective, the architecture choices, and the evaluation methodology, subject to hard physical constraints, not product-manager preference.A small, senior team. You will work directly with world-leading researchers in physics-informed ML and graph-based power systems AI.Competitive compensation benchmarked to tier-1 European AI labs, with meaningful equity in a company with €4M committed capital and growing ARR.Publication and conference travel fully supported.

Senior / Principal Research Scientist – Core AI Algorithms (Autonomous Systems)Location: Germany (Remote-first within Germany, on-site in Frankfurt every 2–4 weeks)About the RoleWe are partnering with a global automotive OEM building a core AI research and algorithm team responsible for the foundational intelligence behind next-generation automated driving systems.This role is research-driven and sits upstream of product teams. The focus is on inventing, validating, and transitioning new perception and world-modeling algorithms from research into production-ready systems. The team operates similarly to a big-tech research lab, but with a clear path to real-world deployment.Research Focus AreasDepending on background and interest, you may work on topics such as:3D scene understanding and world modelingOccupancy, motion forecasting, and dynamic scene reconstructionMulti-sensor perception (camera, LiDAR, radar)Representation learning for autonomous systems (BEV, implicit / generative 3D, Gaussian models, foundation models)Robustness, generalization, and long-tail perceptionLearning under weak, sparse, or noisy supervisionBridging offline training with real-world deployment constraintsKey ResponsibilitiesConduct original research in perception and autonomous systems with clear technical ownershipDesign and prototype novel algorithms and learning frameworksPublish at or contribute toward top-tier conferences and journals (e.g., CVPR, ICCV, ECCV, NeurIPS, ICRA, IROS)Translate research ideas into scalable, production-oriented implementationsCollaborate with applied ML, systems, and hardware teams to ensure feasibilityShape the long-term technical roadmap of the core AI organizationMentor junior researchers and engineers where appropriateRequired BackgroundPhD (or equivalent research experience) in Computer Vision, Machine Learning, Robotics, or a related fieldStrong publication record at top-tier conferences or journalsExperience conducting research within an industrial or applied settingExcellent understanding of modern deep learning methods and 3D perceptionStrong programming skills in Python and/or C Ability to work across the full spectrum from theory to implementationStrongly PreferredResearch experience in autonomous driving, robotics, or embodied AIWork on 3D perception, tracking, SLAM, or world modelsExperience at big-tech research labs, industrial AI labs, or advanced OEM R&DFamiliarity with real-world constraints such as runtime, memory, and system integrationPrior collaboration with product or engineering teamsWhat’s on OfferA research-first role with real influence on production systemsThe opportunity to define core algorithms, not just incremental improvementsA team culture that values publications, patents, and long-term thinkingRemote-first working model within Germany, with regular in-person collaboration in FrankfurtCompetitive compensation aligned with senior / principal research profilesWho This Role Is ForResearchers who want their work to ship into real vehiclesIndustry researchers seeking greater technical ownershipPhD-level candidates who enjoy both publishing and buildingProfiles combining academic depth with practical engineering maturityLooking forward to seeing your profile!

Staff / Principal AI Researcher About the company A deep-tech startup building ultra energy-efficient computing infrastructure for new-generation AI inference. Our brain-inspired chip architecture is purpose-built for dynamically sparse and event-driven algorithms, delivering an order of magnitude better energy efficiency than today's GPU-based systems. Our hardware is already deployed at leading research institutions across Europe and the US, and we are scaling rapidly as the industry wakes up to the energy bottleneck that GenAI is creating. We are looking for talented and passionate people with a real appetite for problem solving to help shape the future of AI hardware. About the role As a Staff/Principal AI Researcher, you will lead the design and development of advanced AI algorithms tailored for our sparse, brain-inspired computing systems. This is a senior, high-autonomy role sitting at the intersection of frontier AI research and novel hardware, with direct influence over both the technical roadmap and how our algorithms reach customers in production. You will lead technically, mentor other researchers, and work closely with our compiler, hardware, and systems teams to make sure our models actually ship and scale on real silicon. What you'll be doingLeading the design, development, and optimization of advanced AI algorithms tailored for sparse hardware and brain-inspired computing systemsArchitecting and implementing efficient machine learning and deep learning models, with a focus on scalability, performance, and hardware-awarenessDriving innovation in algorithmic approaches for sparse and event-driven computing paradigms, especially but not limited to Transformer-based architecturesMentoring and managing a team of AI engineers and researchers, fostering technical excellence and professional growthDeveloping robust, scalable, and maintainable algorithmic frameworks that integrate cleanly with the wider software and hardware ecosystemDefining and implementing benchmarking methodologies to evaluate model accuracy, efficiency, latency, and energy consumptionCollaborating with compiler, hardware, and systems teams to ensure seamless integration and co-optimization of algorithms and execution pipelinesContributing to technical documentation, research publications, and demonstrators that showcase the team's capabilitiesWhat we're looking forProven experience leading cross-functional technical teams in AI/ML development or applied researchDeep expertise in machine learning and deep learning algorithms, including model design, training, and optimization5 years of relevant industry experience developing production-grade AI solutionsExpert-level proficiency with ML frameworks such as PyTorch or TensorFlow, and familiarity with modern model deployment workflowsHands-on experience optimizing models for hardware acceleration (CPU, GPU, or specialized accelerators)Strong analytical and problem-solving skills with a track record of tackling genuinely hard algorithmic challengesBSc or MSc in Computer Science, Artificial Intelligence, Applied Mathematics, or a related fieldNice to havePhD or Dr.-Ing. in a computationally intensive disciplineHands-on experience with DevOps tools and CI/CD pipelinesFamiliarity with MAMBA or other state-space model architecturesHands-on experience with model compression, quantization, or pruningBackground in computer architectureContributions to open-source projects or publications at leading AI venuesFamiliarity with multi-chip computing concepts and techniquesWhat we offer A highly competitive salary, relocation support, and a flexible, inclusive work environment. We are an equal opportunity employer and welcome people of different backgrounds, nationalities, and experiences.