Top Tech Jobs & Startup Jobs

Establishes and improves configuration and change management across the engineering lifecycle. Responsibilities include configuration identification, baselining, change control, status accounting, audits, requirements traceability, Automotive SPICE process support, compliance evidence, tooling, automation, metrics, and guidance for engineering teams.

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.

Develop actionable insights for AI engineering teams using experimental and observational analyses of real and simulated driving. Design metrics, experiments, and off-road measurements; investigate model training and inference bottlenecks; validate improvement hypotheses; and communicate findings that guide product priorities, safety, functionality, and performance. The role requires strong SQL, statistical analysis, data science, and experimentation skills, with machine learning and distributed-computing experience preferred.

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.

Own the technical vision and architecture for a petabyte-scale data enrichment platform supporting machine-learning deployment, inference, evaluation, monitoring, annotation, and retraining. Build reliable backend services, APIs, workflows, and self-service capabilities across large GPU fleets. Lead cross-team architecture and delivery, establish platform metrics, improve reliability and cost efficiency, and mentor engineers.

Lead the technical strategy, design, and production integration of fault detection and fallback systems for autonomous vehicles. Develop robust robotics solutions, collaborate with machine learning and software teams, guide architecture decisions, and improve overall system safety and reliability. The role also involves mentoring, defining acceptance criteria, analyzing fleet data, and potentially building monitoring, debugging, and real-time systems for large-scale driverless vehicle operations.

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.