Top Tech Jobs & Startup Jobs

Build, train, fine-tune, and evaluate computer vision and scene-understanding models for offline autonomous-driving measurement. Improve accuracy and generalization across vehicles, markets, and conditions using large models, temporal context, and multi-task learning. Define ground truth, benchmarks, metrics, and statistically defensible evidence for validation and safety cases. Collaborate across modeling, evaluation, data, and simulation teams while mentoring engineers and driving significant modeling workstreams.

Lead data-centric work for embodied AI: mine and curate fleet driving data, build large-scale enrichment and labeling pipelines, train and fine-tune foundation models, and design rigorous offline and closed-loop evaluation to improve safety and generalization across cities, vehicles, and sensor rigs.

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 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 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.

Lead and grow a Runtime Platform engineering team responsible for application frameworks, middleware/IPC, performance, observability, and real-time scheduling for on-vehicle AI. Hire and coach engineers, set technical direction, own delivery and roadmap, maintain CI/HIL and quality gates, and collaborate with vehicle software, hardware, and OEM stakeholders to enable new platforms with minimal performance degradation.