Top Tech Jobs & Startup Jobs

Build, train, and fine-tune offline scene-understanding models adapted from on-vehicle and foundation models. Improve accuracy and generalisation across vehicles, geographies, and conditions. Exploit offline compute and temporal context, benchmark models with statistically defensible metrics for validation and safety, and lead cross-functional work while mentoring teammates.

Lead development of 3D geometric vision and world foundation models for autonomous driving. Responsibilities include designing and training deep-learning models, building scalable sensor-data, auto-labeling, SLAM, reconstruction, evaluation, and ground-truth pipelines, and deploying models on production vehicles. The role requires technical direction across geometric vision, multimodal sensing, distributed training, neural rendering, 3D representations, and real-time inference.

Own end-to-end delivery of specific ADAS features (e.g., TFL, High Beam Assist, Blind Spot Monitoring). Define feature scope, set performance targets, align OEM requirements with internal roadmaps, collaborate with engineering/product/OEM teams, participate in testing/validation, and mitigate risks to ensure features meet specs and production readiness.

Take end-to-end ownership of specific ADAS features (e.g., High Beam Assist, Blind Spot Monitoring). Define scope and specs, align OEM requirements with internal roadmaps, collaborate with engineering for delivery, participate in testing and validation, mitigate risks, and maintain feature roadmaps to meet production and compliance targets.

Lead Wayve's global marketing for AI Driver technology: define positioning, product narratives, brand strategy, digital and partner co-marketing, content, events, and a small marketing team. Drive integrated campaigns, measurement, budgets, KPIs and cross-functional alignment to support partner adoption and commercial growth.

Build and operate Wayve's security assurance capability: design risk-based assurance methodologies, test and validate control design and operating effectiveness, report findings, track remediation, support external certifications (TISAX, ISO 21434), and partner with engineering to move from periodic to continuous, automated evidence collection.

Design, develop, and deploy end-to-end deep learning driving models focused on performance, robustness, personalization, and collaboration. Build data pipelines, evaluation frameworks, and deployment pathways; mine real and synthetic data; influence architecture and training; collaborate cross-functionally and mentor engineers for production-scale autonomy.

Design and deliver scalable data pipelines for model development and production ML workflows in autonomous driving. Ingest, transform, curate, validate, and monitor large-scale real-world, synthetic, and partner datasets. Improve pipeline performance and collaborate with ML engineers, Data Corpus, AI Platform, and partners to ensure datasets are reliable, traceable, and model-ready.

Lead and deliver complex, cross-functional engineering programs across AI, software, robotics, infrastructure, evaluation, data, and product. Define plans, manage risks and dependencies, improve efficiency and tooling, align stakeholders to business goals, and drive continuous improvement to make multi-team delivery predictable.

Lead end-to-end supply chain execution for prototypes, NPI, and fleet launches. Own material readiness, BOM translation, long-lead identification, supplier compliance, upfitter coordination, risk mitigation, and supply-chain KPI tracking and reporting across Engineering, Operations, and external partners.