Jobs at Wayve

As an Applied Scientist, you'll develop AI systems for autonomous driving, focusing on machine learning, simulation, and robotics innovations. Responsibilities include world modeling, reinforcement learning, spatial intelligence, and cross-embodiment robotics, collaborating within a research team to push advancements in embodied AI.

Build scalable machine learning pipelines, data and training infrastructure, distributed processing workflows, and reusable software systems for foundation-model research. Own codebase health, testing, reliability, maintainability, and performance across ML workflows. Collaborate with researchers and engineers to translate model and experiment requirements into robust systems supporting large datasets, large models, multimodal data, video, robotics, and embodied AI.

Build, operate, and improve Azure-focused cloud infrastructure supporting software development, testing, and production. Implement Infrastructure as Code, maintain CI/CD pipelines, manage containerized workloads, and improve monitoring, observability, security, reliability, and cost efficiency. The role also involves troubleshooting incidents, conducting root cause analysis, partnering with engineering teams, and automating infrastructure and deployment workflows.

Develop and integrate vehicle-facing robot software for autonomous-driving platforms. Responsibilities include vehicle software bring-up, embedded hardware integration, ROS2 development, system debugging, sensor validation, diagnostics, reliability tooling, and architectural improvements. The role collaborates with hardware, safety, controls, calibration, and reliability teams and supports bench, HIL, and on-vehicle testing at international locations.

Develop and maintain Wayve’s embedded Linux distribution, kernel and device drivers for autonomous-vehicle fleets. Build reliable C++ software with strong observability, diagnostics, fault tolerance, and remote debugging capabilities. Collaborate with product, project, hardware, supply chain, and engineering teams. Lead engineering best practices across testing, documentation, tooling, mentorship, and collaboration. Apply expertise in Yocto, boot systems, IPC middleware, robotics frameworks, and embedded systems.

Build, operate, and improve large-scale data ingestion pipelines supporting autonomous driving AI systems. Responsibilities include debugging pipeline failures, handling corrupt and inconsistent data, optimizing Spark jobs, managing orchestration and retries, improving throughput and reliability, reducing operational toil, and supporting downstream annotation, data science, training, and evaluation teams.

Build scalable performance tooling for AI model training and inference across cloud and embedded hardware. Responsibilities include profiling workloads, conducting roofline analysis, identifying layer- and operator-level bottlenecks, predicting latency, memory, utilization, and compute costs, and monitoring regressions. The role requires close collaboration with model, compiler, runtime, platform, and hardware teams to establish performance targets and translate measurements into actionable recommendations.

Own and extend Wayve’s machine learning compilation pipeline from model checkpoint to deployable embedded bundles across NVIDIA TensorRT and Qualcomm QNN targets. Design compiler passes, quantization and precision-typing systems, graph partitioning, legalization, regression testing, and benchmarking infrastructure. Partner with model and training teams to ensure compilability, accuracy, and latency across architectures and SoCs. Provide technical direction and mentorship for compiler engineering.

Research and develop foundation models for general-purpose robotics, including multimodal architectures, scalable training methods, data strategies, and post-training techniques. Build distributed pipelines using large-scale video and robot-interaction datasets, evaluate models on real robots, and collaborate with robotics, hardware, and engineering teams. The role involves frontier research across vision-language-action models, world models, imitation learning, reinforcement learning, and self-supervised learning, with opportunities for publications and ownership of a new embodied AI research program.

The role involves advising on commercial matters, drafting contracts, supporting strategic transactions, and building legal processes as Wayve scales globally.

Develop and ship end-to-end machine learning models for autonomous driving behaviors across L2-L4 systems. Own data curation, training, evaluation, experimentation, and on-road validation. Improve model performance through iterative training cycles while addressing real-world safety, quality, and deployment constraints. The role may involve reinforcement learning, transformer networks, automotive systems, model compression, and embedded deployment.

As a Staff Machine Learning Engineer, you will lead the development and deployment of AI models for autonomous driving, focusing on safety and reliability. Responsibilities include driving the roadmap, training models, and collaborating across teams.

Lead reinforcement learning development for end-to-end autonomous driving models. Responsibilities include designing offline and off-policy RL methods, improving reward models, building large-scale training and evaluation workflows, diagnosing distribution shift and optimization failures, validating policies through simulation and on-road testing, productionizing successful methods, and mentoring engineers. The role also leads development of learned emergency trajectory models for safety-critical maneuvers such as evasive steering and emergency braking.

Own defined portions of Robotaxi system architecture, translating use cases into requirements, interfaces, specifications, and verification plans. Collaborate with OEM, Tier1, and operator partners to establish technical boundaries and lead design discussions. Apply architectures across vehicle platforms, support integration and bring-up, triage issues, assess design changes, and address safety, redundancy, degraded operation, and offboard service considerations for production-intent L4 autonomous systems.

Develop and maintain Wayve’s embedded Linux software stack for autonomous vehicle fleets. Responsibilities include Linux kernel and device-driver development, YoCTO-based distribution maintenance, high-performance C/C++ software development, diagnostics, fault tolerance, debugging, and secure fleet hardening. The role collaborates across engineering, product, hardware, supply chain, and field teams to support reliable on-road experimentation and autonomous driving data collection.

Investigate autonomous vehicle issues across hardware, software, robotics, and AI models; analyze fleet and on-road data, identify root causes, reproduce problems through simulation and testing, and develop automated investigation workflows. Collaborate with engineering, operations, safety, and model development teams, respond to incidents, communicate findings, and improve AV performance and safety.

Leads complex, cross-functional technology programs across AI, software, robotics, vehicle hardware, infrastructure, simulation, data, and product. Owns project delivery, improves efficiency, manages risks and dependencies, aligns stakeholders, supports business goals, and drives continuous process improvement. The role requires strong technical understanding, systems thinking, communication, project management, and influence without authority across engineering teams.

Leads Wayve’s data strategy for autonomous-driving AI, including data acquisition, discovery, curation, enrichment, training, evaluation, deployment, and performance measurement. Builds the data flywheel, identifies rare and safety-critical scenarios, connects data investment to model outcomes, scales global data architecture, and leads a multidisciplinary Data organization. Partners with AI, Science, Validation, Product, Compute, Finance, and Commercial teams.

Build and operate production search and indexing systems at billion-vector scale for multimodal driving data. Develop embedding-based retrieval, metadata filtering, backend APIs, ingestion pipelines, and evaluation frameworks covering relevance, recall, latency, scalability, freshness, and cost. Own components from architecture through production operations, collaborate across ML and data teams, make build-versus-buy decisions, and mentor engineers.

Develop, integrate, tune, and validate vehicle trajectory and motion controllers for autonomous and assisted-driving systems. Build latency-sensitive, production-quality C++ control software, maintain controls architecture, and evaluate performance through simulation, closed-loop testing, and on-road validation. Collaborate across teams to define interfaces, integrate adjacent systems, and support new vehicle platforms and features.