AI Strategy & Executive Advisor – Ticketing & EventsContract: 2–3 monthsLocation: Remote – must be based in EuropeEngagement: ContractWe’re looking for an experienced AI Executive / Strategy Leader to support a business within the ticketing and live events sector on a short-term strategic project.The role will work closely with senior leadership to identify where AI can create meaningful commercial and operational value, and help shape a practical strategy for adoption.The roleWork with senior leadership to assess current AI capabilities and opportunitiesDevelop an AI strategy aligned to commercial and operational prioritiesIdentify high-value AI use cases across ticketing, events, customer experience and internal operationsAdvise on emerging AI technologies and where the business should be investingPrioritise opportunities based on business value, feasibility and potential ROIHelp define an AI roadmap and approach to implementationBring relevant industry insight from ticketing, live events, entertainment or adjacent sectorsExperience neededSenior-level experience in AI strategy, AI transformation, AI leadership or executive advisoryStrong understanding of applying AI commercially within large-scale businessesDeep industry knowledge of ticketing, live events, entertainment or a closely related sectorExperience working directly with C-suite / executive stakeholdersExperience developing AI strategies, roadmaps or transformation programmesStrong understanding of current AI/GenAI developments and their practical business applicationsAble to translate complex AI capabilities into clear commercial opportunities and recommendationsThis is not a hands-on engineering role. We’re looking for someone who can operate credibly at executive level, understands the ticketing/events ecosystem and can provide strategic direction around AI.Applicants must be currently based in Europe.

Research Scientist – Embodied AI | London (Hybrid)upto £200,000 DeepRec.ai is partnering exclusively with a well-funded spatial AI company building a foundation model of the physical world, the layer robots and AI agents need to understand real places, not just pixels. Their London research lab is hiring a Research Scientist into a dedicated Embodied AI group. The focus is closing the sim-to-real gap: turning real-world captures into accurate, simulation-ready environments that robots can be trained and evaluated in. What you'll work onInventing new algorithms at the overlap of computer vision, machine learning, graphics and roboticsAdvancing a large-scale geospatial model for embodied AI use cases3D reconstruction, scene understanding and spatial reasoning for robots and other machinesTaking research from paper to working systems used by real robotics teamsPublishing at top venues alongside a strong, research-led teamWhat they're looking forPhD (or equivalent) in Computer Vision, ML or RoboticsFirst-author publications at CVPR, ICCV, ECCV, NeurIPS, ICLR or similarStrong grounding in 3D vision: SLAM, SfM, neural rendering, Gaussian splatting or similarExcellent research code in PyTorch or JAXBonus: robotics simulation (Isaac Sim, MuJoCo), sim-to-real, VLMs or policy learningWhy it's interestingRare chance to work on embodied AI with real-world data at global scaleResearch-first culture with strong academic linksSmall, senior team where your work ships

safety engineer (m/w/d) Berlin Full-time Permanent employee 90,000 - 100,000 € per year Apply for this job why this role matters Functional safety is what turns coera from a promising product into industry infrastructure. You will own coera's safety case end to end, at component, product, and cell level, in support of customer applications. First CE marking under EU Machinery Regulation 2023/1230 with the notified body is yours to deliver. The certification story we tell in two years starts with the choices you make in month one. what you will ownOwn coera's safety case end to end, at component, product, and cell level, in support of customer applications.Run FMEDA, FTA, HAZOP, and STPA across every coera deployment.Deliver the first CE marking under EU Machinery Regulation 2023/1230 with the notified body.Design coera's safety argumentation: safety goals, functional and technical safety requirements, and the traceability that holds it together.Be the technical authority in safety conversations with customers, integrators, and regulators.what we look for Must haveAt least six years in functional safety engineering, including at least one shipped SIL2 / PLd program. Deep working fluency in IEC 61508 (SIL determination, lifecycle) and ISO 13849 (PLd, Categories, DC, MTTFD, CCF).Working knowledge of IEC 62998, EU Machinery Regulation 2023/1230 conformity assessment, and relevant robotics norms.One of: Certified Safety Machinery Engineer (CSME), TÜV SÜD Functional Safety Certified Professional (FSCP), TÜV Rheinland FS Engineer, or exida CFSE.Hands-on fluency with at least two of: Sistema, Ansys medini analyze, ISOGraph, Polarion, DOORS, LDRA, Vector.Strong signalsStandards working-group contribution: ISO TC 299, IEC TC 44, ISO/IEC JTC 1/SC 42, or UL 4600.Published safety case using claims-arguments-evidence (CAE) or Goal Structuring Notation (GSN).Bridged ISO 26262 (ASIL) and IEC 61508 / ISO 13849 (SIL / PLd) in the same architecture.Shipped a perception-based safety function at SIL2 or PLd.SOTIF (ISO 21448) and ISO/PAS 8800 (AI safety) operational experience.BonusDirect prior work with a notified or certification body.German or Farsi in addition to English.what we offer A core-team role with real ownership over what we build and how. Direct line to deployments with leading robotics partners from day one. Post-thesis, pre-scale: the window where the work you do actually compounds. All coera employees participate in the company's long-term success through our employee participation program (ESOP). The specific terms of your participation are agreed individually as part of your compensation package. how we work Small, senior, deliberate. We write more than we meet. We ship the core of the product before we ship the edges. We care about craft in code, policy design, and how we engage with customers. The bar is high, and the stakes are high: the safety layer of the robotics revolution is being built now, and we intend to be the one that defines it. process and small print Hiring process. A short intro call, a technical conversation tailored to the role, a deeper-dive session (onsite or remote), and an offer. Typical time from application to offer is two to three weeks. We move fast when we meet the right person. Visa and relocation. We support visa sponsorship and relocation within Europe where it makes the difference between hiring the right person and not. Equal opportunity. We hire on the strength of the work. We hire on the strength of the work. We do not discriminate on the basis of ethnic origin, gender, religion or belief, disability, age, sexual identity, or anything else unrelated to the job. We explicitly welcome applications from people with disabilities and give them equal consideration.  Your data. We process your application data under the GDPR to run this hiring process. If we do not hire you, we keep your data for up to six months afterwards so we can respond to any questions or legal claims, then delete it. If you would like us to keep your details longer for future roles, we will ask for your separate consent. You can ask us to delete your data at any time. About us coera is building the universal safety intelligence layer for human-robot coexistence, redefining what safety means in the age of physical AI. Robotics is advancing faster than the safety systems meant to support it. coera closes that gap with multimodal sensing and deterministic guardrails: retrofittable, embeddable, controller-agnostic. Safety is not a checkpoint or a constraint. It is the capability that lets intelligent machines operate freely wherever people do.

Senior Medical Software Engineer  Berlin - 3 days pw in office up to 95k euro base  Hands-on engineering leadership  |  Recruiter brief  |  17 September 2026  The role  Aidvance is developing software for an FDA-regulated medical device that delivers depression treatment through spoken conversations in English. We are preparing a US pilot for 2027.  The priority before the pilot is a structured development process that meets applicable medical-device requirements. We need one hands-on senior engineer to establish that process and lead delivery across backend, mobile app and speech integrations, with support from our existing engineer.  What matters most  • Medical-software development. Strong preference for hands-on medical-device software experience: requirements, risk management, test evidence, controlled releases and technical documentation. Comparable regulated-software experience may transfer. • Engineering leadership. Can establish practical development processes, organize reviews and prioritize delivery. Engineering-management experience is a major plus; this remains a hands-on coding role. • Full-stack delivery. Built and maintained application and backend features, with automated tests, deployment and monitoring. Handles sensitive data responsibly and documents operation and recovery.  Where to look and secondary strengths  Start with medical-device software and digital-therapeutics teams. Healthcare voice/audio teams are also relevant when candidates bring regulated-development experience.  • Provider integration. Can integrate external speech services and test the complete product. Specialist audio experience is optional; a dedicated audio hire can be revisited after the pilot. • Conversation and audio evaluation is a plus. Experience with repeatable tests for conversation behavior, speech quality, response timing and interruptions, using commercial tools or custom setups.  Mobile expertise is secondary, but the role includes developing a new version of our mobile app following medical-device software development practices. Exact languages, speech vendors and specialist model training experience are not hiring filters.

DeepRec.ai is representing a rapidly growing, venture-backed AI company that's building technology to help enterprises understand, improve, and automate complex business processes. Our client's team brings together experienced engineers, AI researchers, and operators with backgrounds building sophisticated AI systems at scale, and they place a premium on technical depth, intellectual curiosity, ownership, and a first-principles approach to hard problems. The Opportunity Our client is building a learning system that helps AI agents understand how enterprises actually operate. Their platform ingests information from sources like knowledge bases, conversations, support tickets, and system activity, then converts it into structured instructions that AI agents can execute — with built-in confidence and reliability mechanisms that determine when an agent should act autonomously versus loop in a human. We're looking to connect them with an AI Engineer who has shipped complex, production-grade LLM systems — whether that's scaling LLM workflows, building multi-agent systems, designing evaluation infrastructure, or developing AI products for demanding production environments. In this role, you'd spend most of your time advancing the company's core AI infrastructure and the systems powering intelligent agents across enterprise use cases, working across continuous learning, agentic workflows, human-in-the-loop feedback, evaluation, and orchestration. What You'd Be DoingBuilding and extending a core context-learning platform, turning real customer problems into reusable AI capabilitiesDesigning and implementing LLM-powered systems and agentic workflows from concept through productionBuilding autonomous agents for knowledge management — systems that can create, edit, update, and maintain large knowledge basesDeveloping reliability and confidence mechanisms, including evaluation frameworks and decision logic for automate-vs-escalate callsArchitecting asynchronous, scalable infrastructure to support complex AI orchestrationBuilding systems that learn and improve through human feedback, evaluation, and iterative optimizationContributing to the company's AI strategy, technical architecture, and product directionWhat Our Client Is Looking ForExperience building complex, production LLM-based systems, and the ability to speak to the engineering decisions and tradeoffs behind themA track record of shipping meaningful software or AI systems to production and iterating on them based on real-world usageExperience building agents, autonomous systems, or sophisticated LLM workflowsGenuine interest in systems that improve continuously through human feedback, evaluation, prompt optimization, and context engineeringComfort operating at the boundary between AI research and production engineeringStrong systems-design chops, particularly with asynchronous and distributed architecturesExcellent written and verbal communication — able to explain technical concepts to both technical and non-technical stakeholders3+ years of professional engineering experienceDon't meet every point on that list? Our client is open to exceptional engineers with unconventional combinations of skills and experience, so we'd still encourage you to apply. Compensation & BenefitsCompetitive base salary and meaningful equityComprehensive health, dental, and vision coverageFlexible PTOSupport for setting up a home workspaceOffice meals, snacks, and drinksAdditional location-appropriate benefitsWorking Environment This is a highly collaborative, fast-paced team with a strong emphasis on in-person collaboration.

Senior Reinforcement Learning EngineerZurich | Hybrid | Full-timeYou’ve already deployed reinforcement learning on real robots. Now you can apply that experience to autonomous excavators working across different machines, sites and soil conditions. You’ll join a Series A robotics company taking Physical AI into construction, with systems already deployed across multiple countries.You’ll build learning-based planning and control systems that work outside the simulator. That means improving simulation and sim-to-real transfer, designing data pipelines for real-world training, running experiments on physical machines and understanding why behaviour changes when conditions get messy. You’ll also integrate learned components into the wider autonomy stack and help shape how the system moves from prototype into a reliable product.This is a hands-on engineering role for someone with 2–5 years of industry experience in reinforcement learning for control or planning, who has actually deployed systems on physical robots. You’ll need strong Python and PyTorch skills, good C++, experience with GPU-accelerated simulation, and the ability to debug real-world robotic behaviour. Experience with hydraulic machinery, large-scale deployments, imitation learning or production rollout strategies would be useful.You’ll have genuine scope to influence the technical direction as the autonomy team builds a long-lived system designed to operate across the construction industry. If you want your RL work to move from simulation into machines doing real work, this is an opportunity to do exactly that.You’ll need:2–5 years’ industry RL experience in control or planningProven deployment on physical robotsStrong Python/PyTorch and good C++Experience with simulation and sim-to-realWillingness to travel when projects require itIf the challenge fits your background, let’s have a conversation about the role and the problems you’d be working on.

DeepRec.ai is supporting a high-growth cybersecurity startup building security solutions for the AI era, focused on protecting AI applications, agents, identity infrastructure, and sensitive enterprise data. (Fully remote position) We're looking for a Threat Researcher with a strong offensive security background to research emerging attack techniques across AI and enterprise environments.   What You'll DoResearch attacks against LLMs, AI agents, APIs, identity systems, and data flowsInvestigate prompt injection, agent manipulation, token abuse, privilege escalation, and data exfiltrationDevelop threat models, attack simulations, and working PoCsBuild research tooling and test environments using Python, Docker, and cloud platformsCollaborate with engineering and product teams to turn research into defensive capabilitiesPublish original research through blogs, talks, advisories, or open-source toolingApply frameworks such as MITRE ATT&CK to emerging AI attack techniquesWhat We're Looking For6–10 years in threat research, red teaming, offensive security, or security engineeringStrong hands-on offensive security and vulnerability research experienceDeep understanding of AI/LLM and agentic architecturesStrong knowledge of IAM, OAuth/OIDC, tokens, privileges, and DLPStrong Python and cloud/container experienceTrack record of publicly shared security research, tooling, or technical writingAbility to communicate complex security research clearly to technical and non-technical audiencesThis is a highly hands-on research role with significant ownership over the research agenda and the opportunity to work on new attack surfaces emerging from increasingly autonomous AI systems.

Principal AI Security Researcher Our client is a fast-growing cybersecurity company building security infrastructure for AI systems. They're hiring a Principal AI Security Researcher to lead and scale their AI security research and red-teaming function. Highly technical leadership role. Candidate should have spent significant time attacking, evaluating, and securing modern AI systems. Sits at the intersection of offensive security, adversarial AI research, and applied engineering. What you'll be doingLead and grow teams focused on AI red teaming, adversarial research, and AI security engineeringDesign and execute advanced attacks against LLMs, GenAI applications, AI agents, and multi-agent systemsResearch prompt injection, jailbreaks, indirect prompt injection, tool abuse, agent manipulation, data exfiltration, model misuse, adversarial behaviorBuild automated systems for continuous AI security testing and adversarial evaluationEstablish methodologies and infrastructure for testing AI systems at scaleLead research into emerging attack surfaces across agent orchestration, tool use, and multi-agent protocolsTranslate research into guardrails, detection mechanisms, monitoring, and security controlsPartner with engineering and product to build security into AI systems through the development lifecycleDefine AI security testing frameworks informed by OWASP, MITRE ATLAS, NIST AI RMFShape technical roadmap and long-term research strategy for the company's AI security platformRepresent the company externally through research, publications, conferencesWhat we're looking forSignificant experience leading security research, offensive security, AI security, or R&D teamsDeep hands-on expertise in AI red teaming, adversarial ML, LLM security, or GenAI securityStrong understanding of how modern AI systems are built, attacked, evaluated, deployedPractical experience researching or exploiting vulnerabilities in LLMs, AI applications, or agentic systemsExperience building security testing frameworks, offensive tooling, automated evaluations, adversarial testing infrastructureComfortable operating at strategic and technical level, close enough to the research to challenge assumptionsTrack record building high-performing technical teams and taking research from concept to implementationStrong communication across engineering, product, security, and executive stakeholders5+ years across cybersecurity, AI security, ML security, adversarial research, or related fieldParticularly interesting backgroundsPublished AI security or adversarial ML researchConference presentations (Black Hat, DEF CON, OWASP events)Open-source AI security contributions, standards, or research communitiesBuilt automated red-teaming or adversarial evaluation platformsDeveloped AI guardrails, runtime security systems, anomaly detection, AI monitoring infrastructureHands-on experience with agentic frameworks and multi-agent orchestrationExperience securing AI workloads across major cloud environmentsBackground in AI trust & safety, adversarial ML, offensive security, or AI researchExperience evaluating frontier models or complex AI/agentic systems in production