Jobs at May Mobility

Design, build, and maintain scalable real-time and historical data pipelines and libraries; implement APIs, data quality checks, monitoring, and performance-optimized workflows; collaborate with autonomy, platform, and front-end teams to support fleet data analysis and visualization.

Design, operate, and scale ML data and training pipelines for autonomous driving. Build distributed training, orchestration, CI/CD and infrastructure-as-code; maintain data/metadata stores and tooling to support large-scale model training and evaluation across cloud and cluster environments.

Design, train, and evaluate advanced machine learning models for autonomous driving, simulation, and ML platforms. Lead small cross-functional engineering teams, establish data and experiment practices for petabyte-scale training, address long-tail model failures, and work with multimodal datasets exceeding 100TB. The role requires commercial robotics experience, mentoring capability, expertise in VLA or generative world models, Python/PyTorch, sensor processing, and potentially edge deployment.

Leads architecture and development of production-grade mapping and localization ML systems for autonomous vehicles. Designs neural models for detection, segmentation, tracking, depth estimation, 3D reconstruction, and semantic mapping; owns data strategy, evaluation, deployment, and fleet validation. Mentors engineers, drives technical roadmaps, collaborates cross-functionally, and advances vectorized HD mapping, foundation models, and real-time optimization.

Leads the technical direction of an autonomous vehicle perception stack. Responsibilities include architecting scalable perception systems, developing scene detection and runtime activation, improving robustness against sensor degradation and adverse weather, integrating multimodal models, managing ML and data pipelines, monitoring field performance, leading testing and feature delivery, and mentoring engineers. Requires strong robotics, computer vision, machine learning, real-time systems, software architecture, and team leadership experience.

The Director of System Safety oversees safety functions for autonomous vehicles, managing safety cases, deployment plans, and regulatory relationships while leading a multi-disciplinary safety team.

Lead architecture and scaling of the AV simulation engine, develop high-fidelity vehicle dynamics and multi-body physics, integrate rendering and sensor-simulation, ensure determinism and performance, own unified evaluation metrics backend, collaborate with HiL partners, lead design/code reviews, and mentor engineers.

Lead and scale simulation operations team to create, tag, execute, and QA simulation scenarios and test suites. Build evaluation metrics, synthesize results into actionable reports, and integrate AI/LLM tools and automation to accelerate scenario generation, bug triage, and reporting for autonomy stakeholders.

The Director of Data Science will lead data-driven initiatives for autonomous vehicle services, overseeing ML evaluations, analytics, and strategic data science objectives. They will manage a team, collaborate with engineering and operations, and ensure high standards in data measurement and experimentation.

Lead the companys autonomous vehicle safety compliance by owning regulatory engagement, NHTSA/SGO submissions, gap analyses against federal/state requirements, and maintaining compliance documentation. Advise Safety and Legal leadership, support incident reviews and investigations, coordinate external counsel, and operationalize federal safety standards and industry best practices into internal processes.

The Director of Product Engineering will oversee product development, strategy, and marketing for autonomous vehicles, ensuring a competitive edge and managing a team of product managers.

Lead perception vision, strategy, and roadmap for AV perception. Drive advances in detection, classification, tracking, fusion, and prediction; design sensor suites; define KPIs; hire and manage engineering teams; coordinate cross-functional deployment and issue resolution to ensure robust commercial autonomy performance.

Lead the design and delivery of a production-grade localization ML stack for autonomous vehicles: architect models for landmark-based localization across sensors, own data and evaluation pipelines, drive feature development to deployment, define failure modes and safety coverage, and mentor engineers while integrating cutting-edge multimodal and foundation-model techniques into robust, real-time localization systems.