Lead and grow the Runtime Platform team, owning its roadmap and delivery while setting technical direction for the frameworks, middleware, performance, and observability that support onboard vehicle software.
Lead commercial execution of robotaxi partnerships, coordinating platform, OEM, and internal teams to launch deployments, manage performance and risks, and scale operations toward production.
Build and own automated hardware-in-the-loop verification for embedded vehicle systems, investigating failures and improving test coverage, reliability, traceability, and release decisions.
Lead the technical programme management function for Data, owning flagship data programmes and roadmap delivery while developing a high-performing TPM team and improving data quality, throughput, cost, and availability.
Own configuration and change management across the full V-model development lifecycle, building practical processes that connect engineering work products, changes and releases with clear control and traceability.
Build and own scalable machine-learning infrastructure for foundation-model research, including data and training pipelines, reusable tools, distributed workloads, and reliable systems that help researchers iterate.
Develop foundation-model architectures, training methods, and scalable data systems for general-purpose robots, translating multimodal machine learning research into robust policies and measurable real-world performance.
Lead research for robot foundation models, shaping the research agenda and technical strategy while developing scalable learning systems, training pipelines, and policies for diverse real-world robots.
Build and own full-stack search, scenario-mining, and agentic evaluation tools that help AI Driver teams find driving scenarios, create tests, debug model behavior, and scale development workflows.
Develop efficient generative world models for autonomous driving, enabling interactive, real-time simulation that supports training, planning, safety evaluation, and deployment across unfamiliar environments.
Develop and improve Wayve’s simulation system for evaluating autonomous driving intelligence, integrating vehicle and driving-stack components while strengthening performance, reliability, reproducibility, and debugging.
Lead a team building synthetic-data capabilities from generative world models, setting technical direction and connecting generation, evaluation, and training to improve autonomous-driving models.
Build and maintain performance-sensitive C++ and Python infrastructure that connects self-driving models with vehicle platforms, ensuring reliable operation, testing, diagnostics, and releases across Linux-based systems.
Leads and grows the US application software engineering organization, bringing AI and autonomous driving technology into customer vehicles from technical definition through integration, validation, release, and production.
Leads Wayve’s Data organisation and strategy, shaping how real-world driving data is acquired, discovered, curated and used to improve AI model training, evaluation and performance at global scale.
Own the engineering processes and tooling that validate, integrate, and release driving models, improving release quality, safety, reliability, and delivery speed across research and engineering teams.
Build and evolve scalable data pipeline and orchestration platforms, including a new DAG-based workflow system, supporting high-volume ingestion, batch inference, evaluations, and reliable workflows across teams.
Lead data enrichment, curation, and rare-event mining for autonomous-driving safety models, while designing out-of-distribution detection and improving the reliability and efficiency of large-scale ML systems.
Investigate complex issues across autonomous-vehicle hardware, software, data, and AI models, tracing incidents to root cause and turning findings into improvements in safety, performance, and investigative processes.
Drive performance across large-scale ML training and cloud inference by profiling workloads, building reusable GPU and framework optimisations, benchmarking gains, and shaping cloud GPU strategy with research and platform teams.
Optimise ML inference for edge accelerators and GPUs, profiling bottlenecks and improving compilers, runtimes and kernels to deliver reliable, efficient production systems for autonomous driving.
Design and deliver production-grade embedded automotive software for autonomous driving, integrating and optimizing the Wayve Driver across vehicle platforms from implementation through fleet validation and production support.
Build and operate reliable, high-throughput data ingestion pipelines for real-world driving data, improving Spark processing, failure recovery and data flow for annotation, model training and evaluation.
Build and operate backend services, APIs, and data workflows that help model engineers run, evaluate, and improve machine-learning models across petabytes of driving data and large GPU fleets.
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