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

AI Product Manager | AI InfrastructureGermany | Hybrid from Heidelberg | €90,000–€100,000 base + performance incentivesYou’ll own the product that decides where and when AI workloads run.This is a chance to take ownership of an AI Scheduling product at a growing AI infrastructure business. You’ll define the multi-year product vision, decide what gets built and work directly with engineers, infrastructure teams and enterprise customers to solve the challenges of running AI at scale.The product sits around some of the hardest practical problems in AI infrastructure: how jobs are queued, how expensive GPU resources are allocated, and how latency, reliability and performance are managed as workloads grow.You’ll have real ownership. Rather than managing a quarterly backlog, you’ll shape the longer-term direction of the product, turn customer problems into clear product decisions and work closely with leadership and engineering to get those decisions into production.You’ll also have direct customer exposure, giving you the opportunity to understand how organisations are actually using AI infrastructure and feed those insights back into the product roadmap.This could suit you if you’re already a Technical Product Manager working on cloud infrastructure, platforms, developer tooling or cluster management and want to take ownership of a product at the centre of the AI infrastructure market.You’ll need:5+ years of Product Management experience, including ownership of infrastructure, platform or developer tooling productsExperience with technologies such as Kubernetes, OpenStack, Slurm, VMware or similarA good understanding of cloud, compute infrastructure and how AI/ML workloads are runExperience creating longer-term product roadmaps and working closely with engineers and technical customersA technical background through education or hands-on experienceExperience in AI/ML infrastructure or MLOps would be particularly relevant.You’ll be based in Germany and able to work from the Heidelberg office on a hybrid basis, with up to 30% travel between Germany and the US.If you’re interested in owning a product that sits at the intersection of AI, infrastructure and resource management, I’d be happy to tell you more.

Senior Systems Performance Engineer – Embedded TechnologyLocation: Amsterdam, NetherlandsWorking Pattern: Hybrid – 2 days per week in the officeSalary: €100,000 – €140,000 per annum + packageJob Type: PermanentThe OpportunityWe’re working with a global technology organisation developing next-generation systems and connected devices, and they’re looking for a Senior Systems Performance Engineer to join a specialist engineering and research team.This is a highly technical role focused on making complex devices faster, smoother and more power-efficient.You’ll work at the intersection of operating systems, system software and hardware, investigating difficult performance problems and developing solutions that can ultimately be implemented and measured on real devices.This isn't an AI model development role. The focus is on low-level systems performance, operating systems and device optimisation.What You'll Be Working OnDepending on your background, you could be working across areas including:Operating system and kernel performance optimisationCPU scheduling and resource allocationProcessor and core selectionPower and thermal managementDynamic Voltage and Frequency Scaling (DVFS)CPU, GPU and memory performanceSystem profiling and performance analysisApplication responsiveness and latencyGraphics, rendering and frame performanceRuntime and framework optimisationHardware/software performance optimisationPerformance improvements across heterogeneous computing environmentsThe team works on complex problems where improvements need to be measured, validated and demonstrated on real hardware.What You'll Be DoingInvestigating complex system-level performance bottlenecksProfiling systems to understand where processing time and resources are being consumedDesigning and implementing performance improvementsOptimising scheduling and resource allocationWorking with CPU, GPU, memory and other hardware resourcesBalancing performance, power consumption and thermal constraintsImproving application responsiveness and system smoothnessCollaborating closely with hardware, chipset, OS and software engineering teamsResearching new approaches to system performance and evaluating their practical valueTaking ideas from investigation and prototyping through to implementation and measurementContributing technical direction to future performance improvementsYour BackgroundWe're open to different technical backgrounds. You do not need to have experience across every area listed above.We're particularly interested in engineers with strong experience in one or more of the following:Linux Kernel / Android KernelOperating SystemsSystem SoftwareCPU SchedulingPerformance EngineeringPower ManagementThermal ManagementDVFSMemory PerformanceGPU / Graphics PerformanceAndroid FrameworksRuntime OptimisationEmbedded SystemsSoC / Chipset PerformanceRelevant titles could include:Senior, Staff or Principal Systems Engineer, Kernel Engineer, OS Engineer, System Software Engineer, Performance Engineer, Platform Engineer, Android Framework Engineer, Runtime Engineer or Graphics Engineer.Your current job title is less important than the depth and relevance of your technical experience.What We're Looking ForWe're particularly interested in engineers who can demonstrate that they have:Solved complex performance problems at system levelPersonally implemented technical improvementsUsed profiling or performance analysis tools to identify bottlenecksMeasured the impact of their work using metrics such as latency, frame rate, power consumption, memory usage or processing efficiencyWorked closely with hardware or chipset teamsDelivered changes that have been deployed to real devices or embedded platformsStrong programming experience, particularly in C/C++ or other low-level/system programming environmentsA typical strong candidate might come from an OS, kernel, Android, embedded, chipset or device-performance background.Why This Role?This is an opportunity to work on technically challenging problems where your work has a direct impact on real-world device performance.Rather than simply analysing performance issues, you'll have the opportunity to investigate the underlying cause, develop solutions and see those solutions implemented and measured on physical hardware.If you enjoy working close to the operating system, understanding how hardware and software interact, and solving problems that require genuine systems-level engineering, this could be a strong fit.Location & Practical DetailsAmsterdam, NetherlandsHybrid working, with approximately 2 days per week in the officeCandidates already based in the Netherlands or willing to relocate are encouraged to applyStrong spoken and written English requiredCompetitive salary in the region of €100,000–€140,000, depending on experience and overall packageCandidates with relevant notice periods are welcome to applyInterested?If your background is in systems, kernel, operating systems, embedded platforms or device performance, we'd be interested in hearing from you.

A fast-growing deep-tech company at the forefront of quantum computing is looking for an experienced Account Manager to become its first commercial hire in the UK and help establish and grow the UK market. You'll work primarily with universities, research centres and technical customers, identifying new opportunities, managing complex sales cycles and building long-term relationships within one of the world's fastest-growing technology markets. ResponsibilitiesIdentify and develop new business opportunities across the UK.Build and manage relationships with academic and research customers.Own the full sales cycle from prospecting through to closing and account growth.Conduct consultative discovery to understand customer requirements and challenges.Translate technical requirements into relevant solutions and value propositions.Work closely with technical, applications, product and marketing teams.Develop account plans and consistently achieve revenue targets.Represent the business at conferences, customer meetings and industry events. Requirements5+ years' experience in technical, consultative or solution-based sales.Proven track record of new business development and revenue growth.Strong technical literacy, ideally within deep tech, engineering or scientific markets.Excellent customer discovery and relationship-building skills.Confident communicating with senior technical and academic stakeholders.Fluent professional English.Proactive, entrepreneurial and commercially driven.Quantum computing experience is highly desirable.Experience selling into universities/research organisations is a bonus. BenefitsUK remote-based with regular travel.Opportunity to build the UK commercial function from the ground up.Competitive salary and performance-based earning potential.Equity opportunities in a high-growth deep-tech business.International exposure and travel to conferences and customer sites.Opportunity to work alongside leading physicists, engineers and scientists.Excellent career progression within a rapidly scaling quantum technology company.

Munich | HybridWe are supporting the growth of a European robotics business building intelligent machines for real-world environments.As their engineering organisation grows, they are looking for a senior technical leader who can take a broad view of the technology behind their robotic systems — connecting software, autonomy, machine learning and the underlying compute platform.This could suit someone currently operating as a Principal Engineer, Staff Engineer, Systems Architect, Technical Lead or Engineering Lead who wants greater ownership of an entire robotics platform.The OpportunityThe challenge here isn't simply developing new robotics capabilities. It's making increasingly sophisticated technology work consistently outside of a controlled development environment.You'll help determine how the platform evolves as the business moves towards larger-scale deployment.That could mean working on questions such as:How should the different software and autonomy components interact?Where are the biggest reliability or performance bottlenecks?How do you make deployment and updates safer and more repeatable?How should engineering teams test increasingly complex robotic behaviours?What needs to change as a system moves from a small number of machines to a significantly larger deployed fleet?How do you create engineering foundations that allow AI and robotics teams to iterate quickly without compromising system stability?You'll have significant influence over these decisions while remaining close to the engineering itself.Your BackgroundWe're open to people coming from several areas of advanced engineering.You may have built systems within:Robotics · Autonomous Driving · Drones/UAVs · Industrial Automation · Edge AI · Embedded/Real-Time Systems · Physical AIMore important than the exact industry is experience building software where AI or autonomous decision-making ultimately has to work on a physical system in the real world.We're particularly interested in people who combine:Strong software engineering fundamentalsSystems architecture experienceRobotics or autonomous systems knowledgeExperience deploying software onto edge/embedded hardwareProduction engineering and reliabilityTechnical leadership across multidisciplinary teamsYou should be comfortable moving between high-level technical decisions and detailed engineering discussions.This isn't intended to be a purely managerial position. You'll be expected to understand the technology deeply, challenge technical decisions and contribute directly where your expertise has the greatest impact.What Makes This InterestingYou'll join at a point where many of the fundamental technical decisions around the platform are still being shaped.Rather than owning one isolated component, you'll work across the wider system and help establish how the company's robotics technology is engineered, deployed and scaled over the coming years.It's particularly relevant for someone who enjoys the intersection of robotics, software architecture and AI, and wants ownership beyond an individual subsystem.Location: Munich, GermanyWorking model: HybridFor more information, apply or get in touch for a confidential discussion.