Paddy Hobson

Senior DevOps Engineer (m/f/d) – Hybrid Infrastructure Location: Berlin, Germany | Employment: Full-time Start: As soon as possible Build the infrastructure behind real-world autonomous driving. 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 Senior DevOps Engineer to develop and operate the hybrid infrastructure supporting its engineering teams, vehicle test data and technical monitoring systems. The role You’ll work across on-premises and cloud environments, taking ownership of Kubernetes infrastructure, automation and observability while helping developers resolve practical problems. A major focus will be preparing the technical monitoring system for 24/7 operation. You’ll also help manage and filter data from vehicle testing and training activities, making it available for analysis and model development. This is initially a hands-on individual contributor role, with the potential to move into team leadership as the function grows. Participation in a future on-call rota will be part of the role. What you’ll do Build, maintain and improve hybrid infrastructure spanning on-premises systems and cloud providers. Manage Kubernetes environments, including AWS EKS, and support reliable containerised workloads. Automate infrastructure provisioning and configuration using Terraform and Ansible, alongside improvements to CI/CD pipelines. Support developers with deployment, infrastructure and environment issues, turning recurring problems into lasting improvements. Develop monitoring, logging, metrics and tracing to give engineering teams clear visibility into system health and failures. Prepare the technical monitoring platform for continuous operation, including alerting, troubleshooting and operational readiness. Manage data lake infrastructure and workflows for ingesting, processing and filtering vehicle test and training data. Evaluate cloud, NeoCloud and GPU-as-a-Service providers against workload requirements, reliability and cost. Support secure access management and reliable communication between services. What you’ll bring At least six years of relevant experience across DevOps, systems administration, cloud infrastructure or platform engineering. Strong hands-on Kubernetes experience, including AWS EKS. Experience operating hybrid environments and understanding the networking and operational challenges between on-premises and cloud systems. Practical knowledge of Terraform, Ansible and infrastructure automation. Experience with AWS, Docker and CI/CD workflows. Hands-on monitoring and alerting experience using Grafana and Prometheus. Strong systems troubleshooting skills and a sound understanding of TCP/IP networking. A collaborative approach to supporting developers, balancing immediate fixes with longer-term platform improvements. A structured working style, curiosity about new technologies and a willingness to learn from experimentation. Professional working proficiency in English; German is a plus. Willingness to participate in on-call support as the platform moves towards 24/7 operation. A background progressing from systems administration into DevOps would be particularly relevant. Useful additional experience Kafka for event handling and data streaming. Data lakes and workflows for large volumes of test or training data. Keycloak or Active Directory for identity and access management. Envoy or service mesh technologies for service communication and observability. OpenTelemetry and the Grafana LGTM stack: Loki, Grafana, Tempo and Mimir. NeoCloud providers, GPU-as-a-Service or infrastructure supporting AI workloads. GitHub and Jira. Cloud certifications from AWS, Google Cloud, Azure or other relevant providers. Mentoring engineers or taking on technical leadership responsibilities. Why join? You’ll help shape the infrastructure connecting autonomous driving development, vehicle data and operational monitoring. Your work will directly influence how effectively engineers build, test and troubleshoot the technology. There’s scope to make meaningful platform decisions, explore new infrastructure providers and establish the practices needed for reliable round-the-clock operation. As the team expands, there may also be an opportunity to grow into a leadership position.

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.

Staff Robot Learning Engineer | Germany | Hybrid | Full-time You’ve already trained and deployed learning-based policies on real robots. Now you can help bring the next generation of humanoid robotics into the real world. You’ll join an ambitious robotics company developing general-purpose humanoids, building robot-learning systems that work beyond the simulator. You’ll train and improve policies, develop data pipelines, run experiments on physical hardware and tackle the challenges of reliable sim-to-real transfer. This is a hands-on engineering role for someone who combines strong machine-learning fundamentals with practical robotics experience. You’ll work across imitation learning, reinforcement learning and emerging vision-language-action models, helping turn promising approaches into robust robotic capabilities. You’ll need: Hands-on experience training robot policies using reinforcement learning, imitation learning or learning from demonstration. Proven experience deploying learned policies on physical robots. Strong Python and PyTorch skills, with good C++. Experience with robot data collection, simulation and sim-to-real transfer. A solid understanding of robot control, action representations and closed-loop policy execution. Experience with humanoid manipulation, teleoperation, diffusion policies, vision-language-action models or real-time action chunking would be a strong advantage. If you want to turn advances in robot learning into capable, reliable humanoid robots, let’s chat!