• Own ML models across their full lifecycle - from data pipelines and feature engineering to training, evaluation, deployment and monitoring; choosing the right metrics and guarding against leakage, overfitting and drift. • Run and improve our ML platform - own the team's GitOps CI/CD and release process, monitor serving endpoints, latency and load in Datadog, and define the SLOs and alerting that keep models reliable in production. • Turn ML into business value across the org - collaborate with risk, operational and product teams to spot and ship ML opportunities, from credit scoring to AI for operational intelligence, sharing your expertise through code reviews and tech watch.

• Own the offline dataset pipeline — design, implement, test, and deploy Cloud-based pipelines that convert logged multi-sensor data into VLM/VLA training datasets, spanning geometric labels (3D/2D detection, tracking, segmentation, depth) through semantic, scenario-level, and action/trajectory-grounded annotations. • Build VLM-assisted auto-labeling — develop open-vocabulary detection, dense captioning, semantic enrichment, and scene/scenario description generation that move beyond closed-set bounding boxes, using foundation models to scale annotation and cut manual labeling cost. • Generate reasoning-grounded labels — produce language-grounded reasoning and chain-of-causation style annotations, temporally aligned to ego-motion and trajectories, to support VLA training and explainable driving behavior. • Mine and curate the long tail — surface rare, difficult, and high-uncertainty scenarios, and build curated datasets that measurably improve downstream VLM/VLA model metrics rather than simply adding volume. • Close the data flywheel — define dataset schemas, quality metrics, and validation; track auto-labeling quality against model requirements; route model failures back into re-labeling and retraining loops. • Partner with the end-to-end model team — co-define dataset specifications with VLM/VLA model developers, own the quality bar and delivery cadence, and operationalize a continuous dataset delivery loop into their training pipelines. • Scale on cloud infrastructure — build distributed, reproducible pipelines using columnar data formats and distributed compute, with disciplined software practices, version control, and documentation. • Lead and mentor — serve as project lead, guide less-experienced engineers, run design reviews, set coding and annotation standards, and drive alignment across team interfaces to the rest of the organization. • Stay current — track the latest advances in multimodal models, auto-labeling, and end-to-end autonomous driving, and translate relevant research into production data systems.

• Provide technical leadership and ship impactful ML-based solutions • Collaborate with cross-functional product squads including product managers, data scientists, and engineers • Design and develop advanced real-time Machine Learning models • Identify and scope impactful opportunities to tackle Financial Crime and Fraud

• Technical leader and individual contributor in Machine Learning • Build and ship models to help customers search for financial products • Develop and deploy advanced ML models to 15 million customers • Involve user and product embeddings and personalised ranking algorithms • Support experimentation and innovation to solve customer problems