Senior Machine Learning Engineer – Localization Autonomous Vehicles | Remote (US or Canada) $187,000 – $250,000 base + bonus + equity About the company Our client is a well-funded autonomous driving company building Level 4 self-driving technology. It has strong long-term backing and is moving toward commercial deployment, so the models built now will run in real-world operation. About the team You’ll join the localization team, which builds the ML models, state estimation algorithms and production software that tell the vehicle exactly where it is and how it’s moving, in real time. The team fuses data from cameras, LiDAR, radar and other sensors, and works closely with perception, mapping, planning and controls. What you’ll do Design, train and deploy production ML models for learned localization, odometry, pose estimation, sensor calibration and map matching Build scalable training and evaluation pipelines in PyTorch on large real-world datasets Improve state estimation and multi-sensor fusion algorithms Analyze large-scale data to find failure modes and drive improvements Write production code in Python and C++ Own features end to end and lead design discussions What we’re looking for Hands-on experience developing your own ML models for localization problems, including architecture, training, loss functions and experimentation Experience with learned localization, odometry, pose or state estimation, calibration or map matching Strong PyTorch and Python skills A solid grounding in 3D geometry, coordinate transforms and probabilistic estimation Experience taking models into production An MS with 3+ years of relevant experience, a PhD with 1+ year, or equivalent Nice to have Traditional localization experience such as SLAM, visual odometry, factor graphs or Kalman filtering, alongside modern ML C++ Distributed training, real-time deployment or uncertainty-aware ML Backgrounds welcome Automotive, robotics, aerospace, drones, industrial robotics and mobile localization are all welcome. Strong PhDs with applied research are encouraged to apply. Why join Build the core localization models for a Level 4 system heading into commercial deployment Develop your own models, not off-the-shelf solutions Work with large-scale real-world sensor data Fully remote, with visa sponsorship available

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.

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.