VP of Agentic SystemsRemote Across Europe We are building a next-generation AI platform powering agent-driven systems and advanced digital creation tools. We are seeking a VP of Agentic Engineering to lead the architecture, scaling, and operational excellence of our core AI platform. This is not a research role. It is a production-scale engineering leadership position focused on building secure, scalable, enterprise-grade systems. What You’ll OwnDefine and lead the Architectural Platform that underpins all AI capabilitiesBuild and scale a wider enterprise platform that ingests, stores, processes, and exposes large-scale data into downstream creation toolsDesign multi-tenant, cloud-native core services and APIsProductionize GenAI systems (LLMs, agent-based systems) into reliable platform servicesLead AI orchestration, model serving, and high-throughput data infrastructureEnsure enterprise-grade reliability, security, performance, and observabilityScale and lead high-performing platform and AI engineering teamsMust HaveProven experience building and scaling enterprise web platformsHands-on experience productionizing Generative AI systems, including LLMs and AI agentsDeep expertise in distributed systems and cloud infrastructure (AWS, GCP, or Azure)Experience designing enterprise-scale data ingestion and storage platformsExperience leading teams of 20 engineers

About the Company We’re supporting a venture-backed AI company building cutting-edge surgical AI systems focused on video understanding. Their initial product transforms surgical videos into structured clinical reports - a focused entry point into a much bigger vision: developing a large-scale surgical foundation model, ultimately enabling advanced perception systems and autonomous surgical robotics.This is a deep-tech team building models from first principles - not treating AI as a black box. They combine ambitious research goals with real-world deployment in high-stakes medical environments. The Role Our client is hiring a Computer Vision Engineer who operates at the intersection of research and production. This is not a pure research role - and not just product engineering. They’re looking for someone who can:Deliver research-grade innovationWrite clean, scalable, production-ready codeMove fluidly between experimentation and deploymentYou will work on state-of-the-art video understanding systems that convert unstructured surgical footage into structured intelligence. This role is central to their long-term roadmap toward advanced autonomy. What You’ll Work OnDesigning and training advanced video understanding modelsExtending image-based CV architectures into temporal domainsWorking with multimodal and potentially 3D data (point clouds beneficial)Building scalable training pipelines, including distributed trainingBridging research prototypes into production systemsContributing to publications at leading AI conferencesCollaborating closely with a highly technical founding teamWhat They’re Looking For Technical BackgroundStrong foundation in Computer Vision on imagesExperience in video understandingExposure to 3D data / point clouds (beneficial)Experience with model training pipelines and optimizationAbility to implement research papers quickly and robustlyStrong software engineering fundamentalsResearch MindsetTrack record (or clear potential) for top conference-level workAbility to derive models from first principlesDeep understanding of modern CV architecturesProblem-SolvingComfortable working in ambiguous environmentsStrong analytical and structured thinking skillsAble to tackle unfamiliar domains effectivelyCollaboration & Product AwarenessUnderstands real-world constraints and client needsComfortable working closely with cross-functional teamsThrives in a collaborative environment (not a solo contributor role_Why Consider This Opportunity?Meaningful Impact - Building AI systems that support safer surgical procedures and improve access to care. Big Technical Vision - Report generation is the entry point. The broader roadmap includes foundational models and advanced autonomy systems. Genuine Deep Tech - This team is building core models and infrastructure from the ground up. Publication & Credibility - Publishing at leading conferences is part of the company’s DNA. Strong Talent Density - You’ll work alongside highly technical peers in an ambitious, research-driven environment.

MLOps Engineer Barcelona or San Sebastián, Hybrid Fixed-term contract until 30 June 2026€45,000-€55,000 Salary €3,000 sign-on bonus €500 per month retention bonus €2,000 relocation support EU work authorisation required Total bonus package available over the contract: up to €5,000 depending on start date. You join one of Europe’s most recognised deep-tech scale-ups. Backed by major global investors and strong EU support, they have built one of the most credible AI compression products in the market. This compression tool is already live with major enterprise clients. Now they need more engineers to help deploy, monitor and scale it properly.Why apply? You will work alongside highly technical quantum and AI engineers operating at a very high level. You will gain hands-on exposure to large-scale LLM deployment, distributed training and real-world cost optimisation. You will have a globally recognised deep-tech brand on your CV, working on AI efficiency at scale. That combination of compression, distributed systems and enterprise deployment opens doors across AI infrastructure, LLMOps and high-performance ML environments. You get flexible working hours. Start early, start late, structure your day how you want. Hybrid setup in Barcelona or San Sebastián. You get meaningful bonuses on top of base salary. What you’ll actually be doing Helping take compressed LLMs and get them deployed, monitored and running reliably for enterprise customers. Improving automation, reliability and cost efficiency across the ML lifecycle. Working closely with researchers and platform engineers to bridge research and production.What you'll needExperience running LLMs in production. Comfort working with the infrastructure around them, cloud, containers, CI/CD, Kubernetes, that sort of thing. Someone who understands what it takes to keep ML systems stable, monitored and efficient once they’re live. If you’ve touched production LLM systems and the infra that supports them, this is likely relevant.

Machine Learning Research ScientistLocation: Waltham, MA (Hybrid. Open to exceptional candidates outside Boston willing to spend approximately one week per month on site)Our client is an early-stage, venture-backed deep-tech company developing next-generation tools for subsurface characterization to accelerate clean energy deployment. Their work sits at the intersection of numerical physics, geoscience, and advanced machine learning, with a specific focus on reducing the cost and uncertainty of geothermal exploration.Founded by experts in physics and computation, the team is intentionally small, highly technical, and academically rigorous. They value first-principles thinking, intellectual curiosity, and a deep personal commitment to climate and clean energy impact. The company has over two years of runway following a recent pre-seed raise and is preparing for its next funding round.As a Machine Learning Research Scientist, you will help build research-grade machine learning models that tightly integrate physical laws with data. You will work closely with domain experts in physics simulation and software engineering to translate geophysical insight into principled ML architectures that can be trusted in real-world energy decisions.This is a selective, fundamentals-driven research role. Our client is not looking for a tooling-only ML profile, but for someone who thinks in mathematics and physics first.Key ResponsibilitiesDevelop machine learning models grounded in mathematical and physical principles to augment numerical physics simulationsDesign and implement algorithms that explicitly incorporate differential equations and physical constraintsCollaborate closely with physicists and engineers to translate geophysical understanding into ML architecturesInfluence the direction of core ML research within a lean, mission-driven teamBuild reproducible research workflows that feed directly into tools for clean energy deploymentRequired ExperienceMust-HavesPhD or equivalent research experience in Mathematics, Physics, or a closely related quantitative fieldStrong mathematical maturity with regular use of linear algebra, differential equations, and numerical methodsFirst-principles problem-solving approach rather than reliance on high-level ML abstractionsStrong Python skills and experience writing clean, research-grade ML codeGenuine motivation for climate, clean energy, and scientifically meaningful workNice-to-HavesExperience in scientific machine learning, including PINNs, operator learning, or surrogate modelingBackground in numerical simulation or high-performance computingExposure to geophysics, subsurface modeling, or energy-domain problemsWhat Success Looks LikeYou can clearly articulate the why, how, and what of your modeling decisions, particularly where physics and ML intersectYou produce reproducible research that improves the speed and quality of subsurface predictionsYou contribute to both foundational algorithms and practical tools used by scientists and engineersInterview ProcessVideo interview with the founding teamOn-site interview with the technical team over one full day

Multimodal AI Systems: Chief Scientist/ Principal Technical Leader We are seeking a senior leader to define and deliver the architecture and research direction for large-scale multimodal AI systems. This role combines scientific leadership with hands-on system ownership, spanning model innovation, training, inference, and production deployment. You will lead the design of multimodal architectures across LLMs, VLMs, video models, and multimodal agents, while driving cutting-edge research in multimodal understanding and generation. The role owns the full lifecycle from novel algorithms and publications to scalable, optimized systems (autotuning, quantization, inference efficiency). RequirementsDeep expertise in multimodal learning with hands-on experience training large-scale vision-language, video, or multimodal models.Strong understanding of transformers, diffusion models, and large multimodal model inference.Proven research impact (top-tier conferences preferred) and/or significant open-source contributions.Ability to translate frontier research into production-grade AI systems.

LLM Algorithm Lead$200,000 - $300,000San Francisco, HybridFull-time / PermanentA product-focused AI start-up is building LLM systems that run in production and are used daily by over a million professionals. This role is responsible for designing, shipping, and maintaining applied LLM systems that support real product features, with an emphasis on reliability, cost, and scale rather than experimentation. Why This Role MattersOwn how LLM systems behave in a large, user-facing productMake architectural decisions that affect reliability, latency, and costMove LLM features from prototype to stable production systemsSet technical direction for applied LLM algorithms and evaluation practicesWhat You’ll DoDesign structured LLM workflows, including planning, reasoning, and multi-step executionBuild and maintain core components such as memory, personalization, and reusable LLM modulesLead development of LLM-powered product features from design through productionBuild and optimize retrieval pipelines (RAG) via chunking, indexing, reranking, and evaluationSelect and route between models based on performance, cost, and latency constraintsDefine evaluation metrics, monitoring, and feedback loopsDebug production issues and drive algorithm-level improvementsWhat You BringExperience shipping LLM-based systems into productionStrong understanding of prompting, reasoning workflows, and system designHands-on experience with RAG systemsExperience building evaluation, monitoring, or safety mechanismsAbility to lead technical decisions and guide other engineersExperience with inference optimization, efficiency, or large-scale systems is a plus

AI Applied Engineer$200,000 - $300,000San Francisco, HybridPermanent / Full-timeA product-led AI start-up is building one of the most widely adopted AI work companions in the world, operating at massive real-user scale with millions of professionals relying on it daily. The challenge problem now is designing AI systems that reliably support complex knowledge work across preparation, collaboration, and follow-through, inside products people trust. This role is ideal for someone who wants to work across AI engineering, product thinking, and ultimately shape how AI actually shows up in day-to-day professional workflows. Why This Role MattersOwn how AI supports high-stakes knowledge workDesign multi-step AI workflows that users rely on repeatedlyHelp define how agent-like systems behave inside a consumer-grade productWork beyond prompt design into evaluation, iteration, and reliabilityWhat You’ll DoOwn the end-to-end design of AI-first workflows for preparation, collaboration, and follow-up Design and iterate multi-step LLM / agentic systems, spanning intent understanding, planning, tool invocation, memory usage, and refinement loopsBuild reusable AI skills, prompts, templates, and evaluation pipelines that can power multiple product experiencesDefine success metrics for AI behaviour, run experiments, and use real interaction data to improve usefulness and reliabilityPartner closely with engineering and ML teams to ship quickly while maintaining a high bar for product quality and user experienceWhat You BringProven experience shipping AI/ML powered products end to endStrong working understanding of LLM systems: prompting, tool calling, retrieval, context construction, evaluation, and common failure modesAbility to translate user needs into clear flows, specs, and examples, including edge cases and expected behavioursComfort working directly with data and interaction logs to debug issues and compare variantsHands-on experience designing agent-like workflows involving multi-step plans, multiple tools, and refinement or self-correction

Agentic AI Engineer$200,000 - $300,000San Francisco, HybridPermanent / Full-timeA product-led AI start-up is building one of the most widely adopted AI work companions in the world, operating at massive real-user scale with millions of daily interactions. The challenge has shifted to designing agent systems that can plan, reason, evaluate themselves, and operate reliably inside real products. This is an opportunity to work from first principles on agentic architectures that power production systems used by professionals globally. Why This Role MattersBuild agent systems that plan, act, reflect, and improve across complex, ambiguous user workflowsDefine foundational patterns for LLM tool-use, reasoning graphs, and self-evaluation in productionJoin at a point where agent architecture decisions will shape the long-term platformWork on problems beyond prompt engineering like runtime reliability, context limits, and learning flywheelsWhat You’ll DoDesign and implement Plan–Act–Reflection style agent architecturesBuild DAG-based reasoning flows to deconstruct user intent into executable stepsDevelop agent skills including function calling, MCP-style integrations, and streaming APIsSolve runtime problems like context overflow / context rot through isolation, compression, and offloading strategiesArchitect automated evaluation and learning pipelines (reward functions, LLM-as-judge, RFT-style systems)What You BringProven experience building and shipping agentic AI systemsStrong understanding of workflow design, failure modes, and deterministic executionComfort designing distributed systems, APIs, and protocols used across teamsPractical experience with agent orchestration frameworks