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.

AI & Digital Solutions Engineer Germany | Permanent, full time | Hybrid working Build practical AI solutions for manufacturing An established German industrial group is creating a new central role to bring software, automation and AI into its engineering, production and business processes. The group has more than 1,000 employees across seven production sites. Its businesses work across industrial production, energy, engineering and transport. AI adoption is at an early stage, giving you the opportunity to identify where technology can improve day to day operations and deliver solutions from the first idea through to production. This is a hands on role for a software developer or AI engineer who understands that the right answer may be a process change, a software application, automation or AI. What you will do • Analyse processes across engineering, production, maintenance, quality, purchasing and administration. • Find and prioritise opportunities for automation and AI. • Build applications using LLMs, RAG, enterprise search and AI agents. • Connect ERP, DMS, production, engineering and Microsoft 365 systems through APIs and integrations. • Develop custom applications, services and automations where existing software does not meet the need. • Evaluate cloud, on premises and open source models, including Microsoft Copilot where suitable. • Help build and operate AI infrastructure, development environments, monitoring and logging. • Take successful proofs of concept into reliable production systems. • Work directly with employees across production, engineering and administration to understand how work happens and where time is lost. Example projects You could help make engineering knowledge easier to find and use, analyse technical documents and drawings, support development and costing, or connect production data with simulation results. Other projects could include linking knowledge about custom tooling, machine manuals, wiring diagrams, fault codes, maintenance history and spare parts. You could also automate recurring ERP and administrative processes. Every solution must account for data privacy, information security and access permissions. What you will bring • Strong practical software development experience, particularly with Python. • Experience building and integrating APIs, SQL databases and business systems. • Practical knowledge of LLMs and generative AI. • Experience with RAG, embeddings, vector databases, AI agents or tool calling. • Confidence taking an application from an early idea or proof of concept into production. • An understanding of manufacturing or production environments gained through relevant work in a manufacturing company. • A practical approach to investigating processes and choosing the right technical solution. • German at B2 level or higher. Native or near native German is preferred because the role involves working directly with production teams and reading German documentation. Experience with C# or .NET, Java, TypeScript, Linux, Docker, ERP, DMS, MES, Microsoft 365, Azure, CAD, CAE or industrial data would be useful. What is offered • A newly created role with room to shape the group’s AI and software architecture. • Direct collaboration with senior management, IT and business teams. • Work across real production, engineering and business processes. • The opportunity to take solutions from process analysis through to production. • Permanent full time employment. • Up to two days working from home each week, with regular visits to production locations. • An Edenred benefits card with a welcome credit and additional credits during the year. The role can be based in Germany. St. Ingbert ideally. They are flex on travel but would prefer candidates based in this region. ding useful AI solutions inside a real manufacturing environment? Get in touch to discuss the role.

Machine Learning Engineer – Inference Optimization Overview We are looking for a Machine Learning Engineer focused on low-latency inference optimization to help build, tune, and productionize high-performance model serving systems. This role sits at the intersection of machine learning, systems engineering, and GPU performance. You will work on inference workloads where latency, throughput, reliability, and hardware efficiency all matter, and where a deep understanding of modern inference runtimes can meaningfully improve production outcomes. You will work closely with researchers and engineers to understand model structure, identify inference bottlenecks, and turn research ideas into efficient production systems. The work may involve other types of models, but focuses on transformer-style architectures and structured inference workloads. You will evaluate and tune frameworks and related serving or compilation systems, while also reasoning about GPU execution, memory layout, batching strategies, precision tradeoffs, and end-to-end latency. What you'll do: Design, build, and optimize low-latency inference systems for production machine learning workloads. Profile model inference pipelines across model execution, runtime configuration, batching, memory movement, serialization, networking, and I/O. Evaluate, integrate, and tune inference runtime systems. Improve latency, throughput, and GPU utilization for production inference workloads. Build and support benchmarking and profiling tools to compare model variants, hardware targets, runtime configurations, and deployment strategies. Debug performance issues involving GPU memory, compute saturation, kernel behavior, CPU/GPU coordination, data movement, and serving-layer overhead. Help shape model and system design choices so that research models are efficient to deploy under real latency constraints. Where necessary, collaborate with lower-level systems or GPU specialists on custom operators, kernel-level optimization, or hardware-specific performance work. What we're looking for: Experience deploying, optimizing, or operating machine learning inference workloads in production or production-like environments. Programming experience in Python, Java, C# etc. and at least one systems language such as C, C++, Rust, or Go. Solid understanding of modern ML frameworks such as PyTorch, including model execution, export, tracing, compilation, and performance profiling. Ability to reason about latency, throughput, batching, memory use, GPU utilization, and reliability under real workloads. Strong practical judgment around tradeoffs between model quality, latency, throughput, implementation complexity, and maintainability. Preferred qualifications: Experience optimizing inference for latency-sensitive or high-throughput applications. Experience with model optimization techniques such as quantization, pruning, distillation, operator fusion, graph lowering, custom operators, or model compilation. Exposure to CUDA, Triton language, ROCm, PTX, CuTe, CUTLASS, FlashInfer, or similar low-level GPU programming tools. Experience running inference workloads on Kubernetes or GPU clusters, including scheduling, autoscaling, observability, and resource management. Background in mathematics, physics, computer science, engineering, statistics, or another technical field. Demonstrated ability to improve real-world inference performance beyond a baseline framework implementation.

Summary An opportunity to join an innovative quantum computing company developing photonic quantum systems for machine learning, generative AI and optimisation. You will develop and implement practical quantum machine learning and optimisation applications, working with customers, research partners and internal engineering teams to take projects from concept through to implementation and benchmarking. This is a hands-on role combining quantum algorithm development, scientific programming and technical collaboration. Key Responsibilities Develop and implement quantum machine learning and optimisation applications for real-world use cases. Design, prototype and benchmark algorithms using the company's quantum hardware and software. Collaborate with customers, research partners and internal engineering teams on technical projects. Evaluate application performance and communicate findings through reports, presentations and publications. Contribute improvements to software tools, algorithms and libraries. Support technical discovery calls and proof-of-concept projects alongside commercial teams. Deliver technical workshops, customer training and onboarding. Document applications and methodologies to support knowledge sharing. Requirements Essential: Master's or PhD in Physics, Computer Science or a related field. Experience in quantum machine learning and/or quantum optimisation. Strong scientific programming skills, particularly Python and Git. Experience with relevant quantum computing and ML frameworks. Ability to independently scope and deliver complex technical projects. Experience collaborating with external technical partners or end-users. Excellent communication skills and the ability to explain complex concepts to different audiences. Desirable: Experience with photonic quantum computing. Quantum hardware and application benchmarking. Hybrid quantum-classical algorithms and architectures. Strong classical machine learning expertise. Publications in quantum computing or ML. Previous customer-facing, technical consulting or pre-sales experience. Benefits & Opportunity Work at the forefront of commercial quantum computing and AI. Hands-on involvement in developing real-world quantum applications. Exposure to advanced photonic quantum hardware and hybrid architectures. Collaborate with leading customers, research partners and engineering teams. Influence software development, applications and technical roadmaps. Competitive salary and benefits package. Hybrid working in Central London.

Machine Learning Engineer, Speech LLMs   You’d be an early member of a new speech LLM lab inside a profitable, bootstrapped company, with the job of training models that understand and generate speech natively.   About the company A profitable Voice AI company based in San Francisco. It builds hardware and software that capture what professionals say and hear, then turn it into notes and usable information. The business has a large global user base and is now setting up a core SpeechLLM lab, which this person would join as a founding hire.   The role You’ll build and train large-scale audio and speech models from the ground up. That could mean unified SpeechLLMs, advanced ASR, expressive TTS or generative audio architectures. One day you’re designing a new sequence modelling architecture, the next you’re debugging a distributed training cluster. The work runs across the whole stack, from signal processing and raw acoustic representations to foundation model training and optimisation for edge devices. You’ll own ambiguous problems and take them into production.   What we’re looking for A proven track record of building and training large-scale audio or speech models. Deep expertise in PyTorch or JAX, including optimising large distributed training runs, managing GPU memory and fixing performance bottlenecks. Comfort moving between research and engineering, and a real interest in AI systems that handle speech natively.   Any of the following is a bonus: Text LLM pretraining, instruction tuning or RLHF Neural audio codecs for streamable, high-fidelity audio Diffusion, flow matching or autoregressive models for speech and voice generation RL techniques such as RLHF or GRPO to improve conversational cadence, steerability and alignment Inference optimisation with serving frameworks like vLLM, TensorRT-LLM or SGLang Large GPU clusters, distributed training with FSDP or DeepSpeed, and Kubernetes   Salary and benefits Up to $270K, plus a performance bonus and equity. Healthcare for you and your dependents, including dental and vision, with a generous employer subsidy. 401(k) with company matching. Unlimited PTO plus 13 paid holidays.   Logistics Full-time, permanent. Based in San Francisco, hybrid, with a minimum of three days a week in the office.