Top Tech Jobs & Startup Jobs

Own the analytical layer for autonomous-driving model evaluation and release decisions. Analyze simulation and on-road experiment data, improve performance metrics and scoring methods, identify measurement gaps, apply statistical rigor, and translate investigations into actionable recommendations. Partner with Data Science, Validation, Product, engineering, and OEM teams to strengthen evaluation coverage, ground truth, quality investigations, and weekly model-release feedback loops.

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.