Summary An opportunity to take on a senior technical leadership role within an innovative deep-tech business working to advance the development of practical quantum computing systems. You will lead the software function, setting the technical direction, overseeing architecture and development, and building a team capable of delivering complex, scalable software solutions. The role combines strategic ownership with technical leadership, working closely with other engineering teams to turn cutting-edge research and technology into operational products.  Responsibilities Software strategy & architecture: Define and own the overall software architecture and technical roadmap, making key build-versus-buy decisions and establishing scalable, modular frameworks. Full-stack software development: Lead the development of the complete quantum software stack, including pulse-level control, automated tuning and calibration, compilation, runtime scheduling and APIs. Team leadership: Build, recruit and mentor a high-performing software engineering team, establishing strong engineering practices, development standards and CI/CD processes. Scalability & fault tolerance: Guide the evolution of software infrastructure and runtime systems to support quantum error correction, real-time classical processing and fault-tolerant computing. Cross-functional collaboration: Work closely with quantum hardware, device physics, semiconductor and quantum error correction teams to translate system requirements into technical deliverables. Manage external software partnerships where required. Requirements Software architecture: Significant experience architecting, developing and delivering complex, modular and maintainable production-grade software systems. Technical expertise: Strong hands-on programming ability, particularly in Python, with a solid understanding of hardware interfaces, runtime performance and system-level trade-offs. Leadership experience: Proven experience building, recruiting and leading software engineering teams, ideally within deep-tech, scientific computing or high-performance computing environments. Strategic ownership: Experience defining technical roadmaps, evaluating technologies and translating broader business objectives into executable development plans. Quantum computing knowledge: Familiarity with quantum computing software stacks, frameworks such as Qiskit, Cirq or OpenQASM, quantum compilers, real-time control or quantum error correction. Benefits Opportunity to work at the forefront of quantum computing and contribute to the development of scalable quantum systems. Join an ambitious, international and highly technical team within a leading European quantum technology ecosystem. Flexible working hours and hybrid working arrangements. 30 days of annual leave, in addition to public holidays. Competitive salary package, including an 8% holiday allowance and stock appreciation rights. Support for international conference attendance, networking and industry partnerships.

AI Hardware + Software Company (Stealth) | San Francisco, CA | Hybrid   About the Role We are seeking a founding-team Senior Inference Engineer to join our core SpeechLLM lab. You will be responsible for building and optimizing high-throughput, ultra-low-latency inference engines for large language models and foundational speech models. This is a rare opportunity to define the architecture of a next-generation AI system that natively understands and generates speech as part of a hardware-software intelligence platform.   You May Be a Good Fit If You Have hands-on experience building and deploying high-throughput, ultra-low-latency inference engines for large language models or foundational speech models. Understand the intricate tradeoffs between latency, throughput, and Time-To-First-Token (or Time-To-First-Audio) in real-time streaming environments. Have practical experience with continuous batching, KV cache management (e.g., PagedAttention), and stateful connections for real-time conversational AI. Possess a deep understanding of GPU architectures (NVIDIA Ampere/Hopper) and the memory hierarchy, allowing you to identify and eliminate hardware bottlenecks. Communicate clearly and collaborate effectively at the intersection of core ML training and backend infrastructure teams. Thrive in fast-moving environments and genuinely enjoy the systems engineering challenge of squeezing every last drop of performance out of a GPU cluster.   Strong Candidates May Also Have Frontier Serving Frameworks: Deep, under-the-hood familiarity with modern LLM serving frameworks such as vLLM, TensorRT-LLM, SGLang, or NVIDIA Triton Inference Server (open-source contributions are a strong plus). Real-Time Audio Streaming: Experience handling continuous audio streams over WebSockets or WebRTC, deploying neural audio codecs, and managing chunked audio generation to minimize conversational latency. Advanced Inference Techniques: Hands-on implementation of speculative decoding, lookahead decoding, or chunked prefill techniques. Model Compression and Quantization: Experience with post-training quantization (PTQ) and deploying models in FP8, INT8, AWQ, or GPTQ without degrading audio naturalness or ASR accuracy. Large-Scale Distributed Systems: Experience deploying multi-GPU (Tensor Parallelism) and multi-node inference pipelines and managing autoscaling infrastructure via Kubernetes.   What We Offer Meaningful early-stage equity and the opportunity to be a foundational member of a core research and engineering lab. Highly competitive base salary: $200K to $540K depending on experience, plus performance bonus and equity. Top-tier healthcare for employees and dependents, including dental and vision coverage with a generous employer subsidy. 401(k) with company matching. Unlimited PTO plus 13 paid holidays. 12 weeks paid parental leave regardless of gender. Hybrid working model with a minimum of 3 days per week in-office. Top-of-the-line laptop or workstation, annual team offsites, and a fully stocked office.

AI Product Manager | AI Infrastructure Germany | Hybrid from Heidelberg  You’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 products Experience with technologies such as Kubernetes, OpenStack, Slurm, VMware or similar A good understanding of cloud, compute infrastructure and how AI/ML workloads are run Experience creating longer-term product roadmaps and working closely with engineers and technical customers A technical background through education or hands-on experience   Experience 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.

FPGA Design Engineer | Quantum Computing Netherlands · Hybrid · Expected salary €80,000–€85,000 How do you control and read out hundreds of qubits in parallel when a few nanoseconds matter? I’m recruiting an FPGA Design Engineer to help solve that problem by building the digital hardware behind large-scale quantum computers. You’ll design RTL for a distributed control system where timing, synchronisation and communication between devices are central to the job. Your work will help determine how precisely the system can control qubits and how reliably it can read them out as the system scales. What you’ll work on Design and verify FPGA logic for parallel qubit control, readout and communication between devices. Tackle nanosecond-level synchronisation across a distributed hardware system. Own FPGA design work from RTL implementation through verification and debugging. Work with engineers across digital design, electronics and software to integrate your logic into the wider system and shape technical decisions. What I’m looking for Hands-on FPGA design experience and confidence writing synthesizable RTL in VHDL, Verilog or SystemVerilog. Experience verifying and debugging digital designs through simulation or on hardware. A solid grasp of digital design fundamentals, including clocking, timing and interfaces. An interest in taking ownership of complex design work and seeing it run on real hardware. If you’ve worked on timing-sensitive FPGA designs in areas such as telecommunications, RF, instrumentation or high-speed data acquisition, I’d be interested in hearing about a design you owned and the hardest problem you had to solve. Apply with your CV or get in touch for a confidential conversation about the role. Curtis Holben DeepRec.ai

AI Engineer – Decision Making Location: Berlin, Germany | Employment: Full-time Reporting to: VP | Start: As soon as possible Build the intelligence that helps autonomous vehicles decide what to do next. DeepRec is partnering with a German autonomous driving company developing a full Level 4 system for European roads. Combining perception, explainable AI, decision-making and vehicle control, the team is building technology designed to make autonomous mobility safe, understandable and ready for certification. We’re looking for an AI Engineer to develop and improve deep learning models for autonomous decision-making and trajectory planning. You’ll join a small team bringing together expertise in reinforcement learning, active inference, deep learning and deployment. The role Your focus will be practical experimentation: implementing model architectures, training and fine-tuning models, reproducing research results and using data to understand what improves performance. A key challenge is improving closed-loop behaviour—how the vehicle performs when a generated plan is executed by the controller and the resulting feedback informs the next decision. You’ll work closely with simulation and other engineering teams to evaluate this interaction and translate findings into better models. Strong hands-on deep learning experience is essential. You should be comfortable moving independently from a research paper or experimental hypothesis to a working implementation, evaluating the results and deciding what to change next. What you’ll do Design, train and fine-tune deep learning models for autonomous driving decision-making and trajectory planning. Translate research papers into working implementations, reproduce experimental results and adapt promising approaches to the vehicle’s requirements. Run structured experiments, tune model parameters and compare results against established metrics and benchmarks. Analyse datasets and model failures to identify gaps in coverage, improve training inputs and strengthen model performance. Work with simulation and control colleagues to evaluate closed-loop behaviour and improve the interaction between planning, execution and feedback. Investigate performance differences between offline datasets, simulation and real-world environments. Collaborate with data, perception and simulation teams to ensure models use relevant data and are tested against meaningful driving scenarios. Support model integration into the wider autonomy stack and, as the platform develops, optimise for latency, memory and compute constraints. What you’ll bring Strong practical experience in deep learning, typically gained through three or more years of relevant work, with the depth of your contribution valued over a rigid tenure requirement. Strong Python skills and hands-on PyTorch experience. A solid understanding of model architectures, training dynamics, optimisation and evaluation. The ability to implement research independently, reproduce results and systematically investigate why an experiment succeeds or fails. Experience applying learning-based methods to trajectory planning, decision-making or closely related autonomous systems problems. The ability to use data analysis to improve models, including identifying failure patterns and making informed changes to datasets, training or evaluation. An understanding of generalisation challenges when moving between datasets, simulation and real-world conditions. An open-minded, experimental approach and the ability to collaborate closely with engineers across disciplines. Automotive or autonomous driving experience is particularly valuable; relevant robotics experience is also welcomed. Previous production deployment experience is useful, but not essential if you bring strong model-development and experimentation skills. Useful additional experience Reinforcement learning or active inference. Autonomous driving simulation environments such as CARLA. Experiment tracking with tools such as Weights & Biases or MLflow. ROS/ROS 2 and C++ integration. Model deployment and optimisation using ONNX, TensorRT or quantisation. Training in GPU compute environments and working with Git, Docker and CI workflows. Why join? You’ll work on a central challenge in autonomous driving: turning learned models into decisions that perform reliably as the vehicle interacts with its environment. In a small, multidisciplinary team, you’ll have direct influence over experiments, model choices and evaluation methods. The immediate opportunity is to improve closed-loop performance, with further scope to tackle efficient inference on constrained vehicle hardware as the platform matures. Interested? Contact DeepRec to discuss the role confidentially.

Robotics Software Integration Engineer – C++ / ROS 2 Location: Berlin, Germany | Employment: Full-time Start: As soon as possible Help bring the autonomous driving stack together on real vehicles. DeepRec is partnering with a German autonomous driving company developing a full Level 4 system for European roads. Combining perception, explainable AI, decision-making and vehicle control, the team is building technology designed to make autonomous mobility safe, understandable and ready for certification. We’re looking for a Robotics Software Integration Engineer to help turn contributions from multiple engineering teams into reliable, integrated software releases. As the fleet grows from approximately five to six vehicles towards 50+, you’ll play a key role in keeping the software platform stable and development moving. The role Working across robotics and autonomy teams, you’ll combine practical C++ and ROS 2 knowledge with ownership of integration workflows, technical reviews and build quality. You’ll understand how changes to sensor drivers, perception pipelines and middleware affect the wider system, identify integration issues early and work with developers to resolve them. Alongside technical investigation, you’ll keep repositories, pull requests and dependencies aligned with release plans. This role suits an engineer who enjoys understanding how a complete robotics system fits together and takes responsibility for getting software integrated, tested and ready for deployment. What you’ll do Coordinate software integration across teams, tracking repository status, pending changes and dependencies between packages. Manage the pull-request lifecycle, following up on reviews, checking merge readiness and keeping merge queues moving. Perform first-pass technical reviews of C++ changes involving sensor interfaces, perception pipelines, ROS 2 packages and middleware. Identify interface mismatches, dependency conflicts and integration blockers, working directly with contributors to resolve them. Maintain GitHub workflows, including branching practices, issue tracking, tagging and release preparation. Monitor and maintain CI pipelines, investigate build failures and coordinate fixes in containerised development environments. Support Docker and Kubernetes environments used for development and integration. Work with engineering teams to keep changes aligned with integration requirements, quality standards and release timelines. What you’ll bring At least four years of professional experience in robotics software, software integration or embedded software environments. A degree in Robotics, Computer Science, Embedded Systems, Electrical Engineering or a related discipline. Practical C++ development experience in Linux environments. Hands-on experience with ROS 2 and robotics integration workflows, including packages, launch configurations, lifecycle concepts, coordinate transforms and sensor pipelines. Experience with Git/GitHub, pull-request reviews, branching strategies and collaborative development. An understanding of sensor integration and how perception, localisation and control interfaces connect within a robotics system. Familiarity with middleware, distributed systems and publish/subscribe communication. Exposure to CI/CD, Docker and automated build systems. An understanding of communication protocols used in robotics and embedded systems, such as CAN, Ethernet, TCP/IP, UDP, SPI, I2C and UART. Clear communication, structured problem-solving and the persistence to follow integration issues through to resolution. Useful additional experience Automotive or autonomous driving software integration. Diagnosing failures in deployed vehicles, robots or other live systems. C, Python or Bash. Kubernetes. Embedded systems, real-time operating systems or hardware-facing software. Working across several engineering teams with shared dependencies and release deadlines. Awareness of safety standards and development practices that support certification. Why join? You’ll work at the point where individual autonomy components become a functioning vehicle system, with visibility across sensors, perception, planning and control. As the fleet scales, your work will directly influence release reliability, development speed and the team’s ability to diagnose and resolve problems on real hardware. It’s an opportunity to deepen your understanding of the full autonomous driving stack while taking meaningful ownership of how it comes together. Interested? Contact DeepRec to discuss the role confidentially.

Team Lead – Software Integration & Robotics (C++) Location: Berlin, Germany | Employment: Full-time Reporting to: VP | Start: As soon as possible Help turn autonomous driving technology into a reliable, scalable vehicle platform. DeepRec is partnering with a German autonomous driving company developing a full Level 4 system for European roads. Combining perception, explainable AI, decision-making and vehicle control, the team is building technology designed to make autonomous mobility safe, understandable and ready for certification. We’re looking for a Team Lead to own the software infrastructure that connects this technology on the vehicle. As the fleet grows from approximately five to six vehicles towards 50+, you’ll help establish the architecture, integration processes and engineering standards needed to scale reliably. The role You’ll lead and develop the Software Integration & Robotics team, owning the layer that connects perception, scene understanding, decision-making and motion planning with sensors and drive-by-wire hardware. This is a hands-on leadership position, with approximately 40–50% of your time spent on technical delivery. Alongside architecture and implementation, you’ll coach engineers, own delivery commitments and work across four to five neighbouring teams to establish consistent engineering practices. Success requires strong technical judgement and the ability to bring people with you: explaining decisions clearly, building agreement and ensuring standards are followed across team boundaries. What you’ll do Lead and develop software integration and robotics engineers through regular feedback, coaching, performance management and a team environment where concerns can be raised openly. Define and implement the integration architecture, including communication between software components and interfaces to vehicle hardware. Own the integration backlog, release deadlines and dependencies across perception, motion planning, simulation and other engineering teams. Set coding guidelines, lead code reviews and establish CI/CD quality gates that support reliable system-level releases. Make practical decisions about build systems, compilation, toolchains and how the integrated platform is tested. Improve real-time data logging, debugging and offline testing to identify issues earlier and resolve failures on live systems. Contribute to the selection and integration of target compute hardware, considering embedded constraints, performance and future fleet growth. Balance immediate delivery needs with an architecture that supports a larger fleet and certification readiness. What you’ll bring Proven experience leading an engineering team, including developing mid-level engineers and taking responsibility for delivery. Strong modern C++ skills, with experience using C++17 or later in production systems. Hands-on experience integrating software with physical hardware, ideally in automotive or autonomous driving; relevant robotics experience is also welcomed. A strong understanding of embedded and real-time software, with the ability to coach others in these areas. Experience with Linux/Ubuntu, ROS, Docker, CI/CD and TCP/IP communication. Practical ownership of code quality, including pull-request reviews, automated checks and engineering guidelines. Experience diagnosing production failures and improving the reliability of deployed systems. The communication and influencing skills to align multiple teams around technical decisions and hold a consistent engineering standard. A willingness to stay close to the code and hardware while providing clear direction and dependable people leadership. Useful additional experience ROS 2 and software architecture ownership. Real-time Linux kernels or RTOS environments. Automotive safety standards and development practices that support certification. AUTOSAR Adaptive, MISRA or embedded platform and hardware selection. GPU performance optimisation or AI model deployment on vehicle compute hardware. Why join? You’ll own a foundational part of an autonomous driving platform: the software infrastructure that allows individual components to operate together reliably on a real vehicle. With a small team, direct visibility to the VP and a fleet preparing to scale, your decisions will shape both the technical platform and the way engineering teams build and release software. It’s an opportunity to combine meaningful people leadership with substantial hands-on ownership—and see the results running on vehicles. Interested? Contact DeepRec to discuss the role confidentially.

Senior Infrastructure Engineer London (Hybrid, 3 days/week in office) | Permanent | £110,000–£130,000 base + 15% bonus + benefits About the Company I’m supporting a confidential client developing AI software that helps protect people from telephone fraud. Its products apply voice AI to identify suspicious activity and help users make safer decisions during calls. The business is moving from product development into commercial deployment, working with telecommunications organizations. A small engineering team brings together backend and AI expertise, with this hire adding dedicated infrastructure ownership. About the Role As the first dedicated infrastructure engineer, you’ll establish the foundations for a managed production service and take responsibility for how it runs as adoption grows. The immediate priority is deployment, followed by improvements to reliability, performance, resource utilization, and cost. You’ll work closely with backend and ML engineers, alongside external engineering teams. This is a hands-on position with scope to shape infrastructure standards, make architectural decisions, and carry those decisions through to implementation. What You’ll Do Build and operate AWS infrastructure for production services, covering GPU inference and supporting CPU workloads. Own development and staging environments across AWS, with some Azure usage. Manage Kubernetes deployments through GitOps workflows using Helm and ArgoCD. Develop repeatable deployment, upgrade, and support processes for customer environments. Establish monitoring and alerting across internal environments and deployed production services. Improve capacity, performance, resource utilization, and infrastructure costs as usage increases. Create operational runbooks and incident response procedures, working with engineers to resolve reliability issues. What We’re Looking For Strong hands-on AWS experience, including independently establishing and operating infrastructure. Evidence of owning a production deployment and scaling it through an early period of growth. Practical Kubernetes experience and an understanding of reliable container orchestration. Experience with infrastructure as code, ideally Terraform, and GitOps deployment workflows. Experience monitoring production systems using Prometheus, Grafana, or comparable tools. The ability to investigate performance issues and make informed reliability and cost tradeoffs. Confidence working through incomplete requirements and taking responsibility for delivery in a small team. Nice to Have Experience with Azure alongside AWS. Kubernetes-based MLOps tooling, such as Kubeflow. Experience operating GPU workloads, including drivers, scheduling, and utilization. Familiarity with AI engineering, voice applications, or telecommunications systems. Experience deploying into air-gapped customer environments. Knowledge of secrets management and least-privilege access across environments. Why Join Infrastructure ownership. Establish the technical foundations and operating practices as the first dedicated infrastructure hire. Visible impact. Support a commercial launch and see how your decisions affect service reliability and growth. Meaningful application. Build systems behind technology designed to help protect people from fraud. Broad technical scope. Work across cloud infrastructure, GPU services, deployment automation, and production operations. Close collaboration. Work directly with backend and ML engineers, with room to influence technical direction. What to Expect This is a small team moving toward commercial delivery, so priorities will evolve and some problems will arrive without a complete specification. You’ll need to be comfortable making practical decisions and implementing them with limited existing infrastructure support. The role is based in London, with three office days each week. Previous AI or telecommunications experience is useful but is not required. The package includes a £110,000–£130,000 base salary, a 15% bonus, private medical cover, life insurance, an 8% matched pension contribution, subsidized food delivery on office days, and office refreshments. Candidates will need an existing right to work in the UK, as sponsorship is not expected to be available for this appointment. Interview Process The expected process is: An initial phone screening conversation. A second phone screening conversation. An on-site technical interview covering technical questions and hypothetical scenarios. An on-site conversation about working style and team fit, with an opportunity to meet the wider team. Get in touch with Curtis Holben at DeepRec.ai for a confidential conversation.