About the roleWe are supporting a Munich-based deep-tech company building low-power distributed sensing devices.Their devices need to communicate over long distances while remaining power-efficient and robust in challenging environments.They are looking for an RF / Communications Engineer to work alongside their current communications engineer and help take ownership of the wireless networking side of the platform. This role sits between RF, embedded systems and IoT networking.What you’ll doWork on low-power wireless communication for distributed sensing devices.Develop and improve LPWAN / long-range communication systems.Work with sub-GHz communication technologies.Support routing, networking and communication protocols across distributed nodes.Help improve resilience against interference, jamming and spoofing.Work closely with embedded systems and hardware teams.Support system-level testing and debugging of communication performance.Help scale the communications architecture as the device network grows.What they’re looking forExperience with RF communications or wireless embedded systems.Understanding of LPWAN, sub-GHz communication or long-range IoT networks.Knowledge of routing, networking or distributed communication systems.Comfortable working with low-power devices.Ability to work close to hardware and embedded engineering teams.Practical engineering mindset with strong debugging ability.Nice to haveExperience with jamming/spoofing resilience.Experience in defence, aerospace, drones, robotics or industrial IoT.Experience with LoRa, LoRaWAN, mesh networking or proprietary sub-GHz protocols.Experience with field testing wireless communication systems.

About the roleWe are supporting a Munich-based deep-tech company building distributed acoustic sensing systems. Their technology needs to be tested in real-world environments, including field trials, drone tests and controlled chamber testing.They are looking for a Flight Test / Data Engineer who can take ownership of the data side of these tests. This person does not need to come from a flight test background; the most important skill is being able to work with real-world sensor data, process it properly, structure it, score it and identify errors in the data pipeline.What you’ll doSupport acoustic data collection from field tests, drone tests and chamber testing.Work with drone operators and interns to conduct structured test campaigns.Record, process and organise test data.Perform post-processing and quality checks on acoustic sensor data.Score and structure results in databases.Identify, debug and document errors in the data collection and processing flow.Help improve how test data is captured, labelled, stored and used by engineering teams.Work closely with ML, embedded and hardware engineers to make test data usable.What they’re looking forStrong data engineering or data processing experience.Comfortable working with messy, real-world sensor data.Experience with databases and structured data workflows.Strong Python or similar scripting skills.Ability to debug errors across data collection, storage and processing.Practical mindset; happy to be close to real-world testing rather than only working behind a desk.Interest in learning flight testing and drone-based data collection on the job.Nice to haveExperience with acoustic data or signal processing.Experience with drone testing, robotics testing or field testing.Experience in defence, aerospace, robotics, autonomous systems or industrial sensing.Experience supporting ML teams with high-quality labelled/structured data.

About the roleWe are supporting a Munich-based deep-tech company building a distributed sensing and AI platform. Their hardware collects real-world sensor data, which needs to be stored, processed, managed and connected through a robust cloud infrastructure.They are now looking for an internal Cloud Infrastructure Engineer to own their AWS cloud setup from first principles. External partners have helped define proposals and support DevOps execution, but the company now needs someone internally who can own the architecture, strategy and long-term knowledge base.What you’ll doOwn the AWS cloud infrastructure strategy and architecture.Build cloud infrastructure from the ground up.Define scalable, secure and maintainable cloud systems.Work with Terraform or similar infrastructure-as-code tooling.Coordinate external cloud/devops partners and review their work.Take ownership of cloud decisions around EU data sovereignty and infrastructure design.Support ML/data infrastructure for sensor data and AI workflows.Build internal knowledge so cloud ownership does not sit only with external contractors.Decide what should be built internally versus delegated externally.What they’re looking forStrong AWS infrastructure experience.Experience building cloud infrastructure from scratch, not just maintaining a small part of an existing system.Strong Terraform / IaC experience.Good DevOps fundamentals.Comfortable owning architecture, strategy and hands-on implementation.Experience working with external engineering partners or contractors.Ability to think like a builder: practical, hands-on, and comfortable creating structure where little exists yet.Nice to haveML infrastructure or MLOps experience.Experience with sensor data, IoT data or edge-connected devices.Experience with EU sovereignty, security or regulated data environments.Startup or early-stage company experience.

About the roleWe are supporting a Munich-based deep-tech company building distributed sensing systems that combine low-power embedded hardware, acoustic data acquisition, wireless communication and AI at the edge.They are looking for a full-time Electronics Engineer to take ownership of the electronic design work behind their main sensing node. Until now, parts of the electronics work have been handled by a mix of internal engineers, external design partners and contractors. The goal now is to bring this knowledge in-house and have one person own the design, development and ongoing improvement of the hardware platform.What you’ll doOwn electronic design for low-power embedded sensing systems.Create and maintain schematics for new PCB versions.Work on low-noise analogue data acquisition systems.Support PCB design, routing decisions and design reviews.Maintain the internal electronics knowledge base.Work closely with embedded systems, RF/comms and ML teams.Help improve reliability, power consumption and manufacturability of the sensing node.Interface with external electronics partners where required, while gradually bringing knowledge in-house.What they’re looking forExperience designing electronics for embedded, IoT or low-power connected devices.Strong schematic design experience.Good understanding of analogue systems and low-noise data acquisition.Experience with PCB development and iteration cycles.Comfortable working across sensors, batteries, networking and edge hardware.Able to take ownership rather than only contribute to small isolated design tasks.Practical mindset; someone who enjoys building, testing and improving physical products.Nice to haveExperience with acoustic sensing hardware.IPC Class 3 exposure.Experience with battery-powered devices.Experience in defence, robotics, industrial IoT, wearables or connected sensor systems.

🚁 Hiring: Drone Sales Specialist📍 Remote – North America or Europe✈️ Travel to customers and industry events(OPEN TO ALL SENIORITY LEVELS)I’m supporting a global battery technology and manufacturing business that is expanding its commercial team across the drone and UAV market.The role will focus on opening new customer relationships, managing the full sales cycle and working closely with engineering teams to align product performance with customer requirements.Key responsibilities:• Develop new business with drone OEMs, UAV manufacturers, defence customers, pack integrators and component suppliers• Manage opportunities from initial engagement through testing, qualification, RFQ and production order• Build relationships across commercial, defence, public safety and advanced mobility markets• Attend major drone and aerospace events to generate pipeline• Work with technical teams around flight time, payload, energy density, safety and performanceThe ideal person will already have strong relationships across the drone ecosystem and a proven track record of converting technical customer opportunities into revenue.Battery experience is beneficial but not essential. Experience across drones, UAVs, eVTOL, defence, aerospace or autonomous systems is highly relevant and ideally a must have for this sales related role.The role can be tailored around seniority, with strong commission upside.

AI Knowledge Graph LeadUK up to £140,000 baseWe’re partnering with an AI-driven technology company looking to hire an AI Knowledge Graph/ Ontology Lead to build and scale the knowledge graph layer underpinning a new generation of intelligent enterprise products. This is a senior, hands-on leadership role for someone who has real experience designing and shipping production knowledge graphs, not just conceptual ontology work. You’ll be responsible for shaping a graph and ontology platform that powers: • AI retrieval and RAG workflows • Entity linkage and reasoning systems • Cross-domain and temporal knowledge modeling • Regulatory and compliance intelligence products • Agentic AI applications The role combines architecture, hands-on engineering, and team leadership. You’ll work closely with AI/ML engineers to turn complex, unstructured information into structured, queryable intelligence that directly feeds live AI systems. Key experience includes: • Strong Neo4j and Cypher expertise in production environments • Deep ontology / taxonomy modeling experience • Python engineering skills • Knowledge graph integration with LLMs, RAG, or vector search systems • Experience balancing formal semantics with pragmatic implementation • Ability to lead technically while remaining hands-on Additional experience in areas such as RDF/OWL, inference engines, entity resolution, legal/regulatory data, ESG, healthcare, or pharmaceutical domains would be highly valuable. The company offers: • A highly technical AI environment • Significant ownership and architectural influence • Fast decision-making and direct access to leadership • Strong adoption of AI-assisted development tooling • Flexible working arrangements and competitive compensation Ideal for someone excited by the challenge of building the intelligence layer behind complex AI products at scale. Please apply for more details

Agentic AI EngineerParis, onsite five days per weekWe are supporting an early stage AI company building a knowledge and decision layer for factories.The platform ingests documents, logs, images, and video, then converts this information into an internal ontology and graph that AI agents can use to reason, call tools, and support operational workflowsYou will help design the core intelligence layer, from data ingestion and modelling through to graph architecture, agent orchestration, evaluation, and production reliability.This is not a basic RAG role. You will need to understand how information is structured, how relationships are represented, and how agents navigate complex systems.You should bring strong Python and software engineering skills, experience with data modelling, graph based systems or knowledge graphs, and production experience with applied AI. You should also understand tool calling, agent workflows, prompt design, and evaluation frameworksExperience with Graph RAG, Langfuse, ontologies, multimodal data, manufacturing, robotics, or industrial software would be useful.Founding engineer or technical founder experience is also valued.Salary is expected to sit around €80,000, with flexibility towards €100,000 or above for exceptional profiles. Equity may also be available.If your open to this position, feel free to apply to this JD with your updated resume and we'll be in touch to discuss further if there is alignment.

About our client:Our client is a fast-scaling automation platform that operates cloud-native and AI infrastructure at scale. By embedding autonomous decision-making directly into Kubernetes and cloud environments, the platform continuously optimizes performance, reliability, and efficiency in production, replacing tickets, alerts, and manual tuning with continuous automation that adapts infrastructure as conditions change. The company is trusted by over two thousand organizations, including a number of globally recognized enterprises across technology, automotive, media, and financial services. It operates as a distributed, international team spanning more than thirty countries across Europe, North America, Latin America, and APAC. The business recently reached unicorn status following a strategic investment from a major corporate venture arm, with a valuation now in excess of one billion dollars and strong momentum behind its next phase of growth. About the role:  Throughput. Latency. KV cache utilization. Move those three numbers in the right direction, and two things happen. Customers get faster, cheaper inference, and our client's margins improve. That is the entire thesis of this role. Every kernel you tune, every quantization scheme you ship, and every scheduler tweak you land shows up directly in a customer's p99 and on the P&L. This is a high-impact seat, and a high-autonomy one. You will be given the room to lead the technical direction of inference optimization rather than execute someone else's roadmap. The problem is that running LLMs in production is a moving target. The right model and serving configuration for a workload depend on traffic shape, sequence-length distribution, batch dynamics, GPU SKU, memory bandwidth, quantization tolerance, and a dozen other variables that shift week to week. Most teams pick a model once, over-provision GPUs, and absorb the cost. Our client's system makes that decision automatically, continuously matching workloads to the most cost-efficient, best-performing LLM and serving configuration on a customer's infrastructure. The team is building the optimization layer between the model and the hardware, and needs engineers who understand both sides deeply. Stack Python; vLLM; SGLang; TensorRT-LLM; PyTorch; CUDA-adjacent tooling; Kubernetes; gRPC; ClickHouse; PostgreSQL; GCP Pub/Sub; AWS, GCP, and Azure; GitLab CI; ArgoCD; Prometheus; Grafana; Loki; Tempo. RequirementsFive or more years building real ML systems, with a portfolio that shows depth in inference or training infrastructure, not just model training notebooks.Strong Python, with experience building production services rather than scripts.Hands-on experience with at least one of vLLM, SGLang, or TensorRT-LLM, and a working mental model of why an inference engine performs the way it does on a given GPU.Fluency with quantization tradeoffs. You have measured quality regressions, not just compression ratios.Comfort with distributed systems, including collective communication, sharding strategies, and the practical failure modes of multi-GPU and multi-node setups.A bias toward measurement. You instrument before you optimize, and you can tell the difference between a real win and a benchmark artifact.Self-direction. This role comes with a wide mandate, and you should be excited by that rather than unsettled by it.ResponsibilitiesPush throughput. Continuous batching, speculative decoding, chunked prefill, and kernel-level tuning across vLLM, SGLang, and TensorRT-LLM. Find the ceiling on each GPU SKU, then raise it.Cut latency. Attack TTFT and TPOT separately. Profile, identify the actual bottleneck whether compute, memory bandwidth, scheduling, or networking, and fix it rather than the bottleneck you assumed.Get more out of the KV cache. Paged attention, prefix caching, eviction policies, cache reuse across requests, and quantized KV. This is where a lot of the unrealized throughput lives, and it is an area you will own.Quantize without regressing quality. INT8, INT4, and FP8 across weights, activations, and KV. Empirical work that measures quality on real workloads, not just perplexity benchmarks.Shrink cold starts and memory footprint. Faster init, smarter weight loading, and tighter memory accounting, which is the difference between a model that scales and one that does not.Scale across nodes. Distributed inference topologies, network-aware placement, and checkpointing strategies that do not bottleneck on storage or interconnect.Set the technical direction. Decide what to benchmark, what to adopt, and what to build in-house. Bring the team along with strong writeups and reproducible experiments.