May Mobility

General Counsel

• Draft, review, and negotiate a range of commercial and supplier agreements, including hardware supply, software licensing, services, joint development, and partnership agreements, while developing and maintaining playbooks, templates, and negotiation guidelines for use across the legal team
• Design, implement, and maintain compliance programs and policies covering areas such as data privacy, safety, regulatory obligations, and corporate governance
• Provide day-to-day legal counsel to cross-functional teams including engineering, operations, business development, government affairs, marketing, and human resources
• Advise on regulatory matters related to autonomous vehicle testing, deployment, and commercialization at the federal, state, and local levels
• Support the management of outside counsel and coordinate with external advisors on specialized matters including litigation, IP, and regulatory filings
• Monitor and analyze evolving laws and regulations affecting autonomous vehicles, transportation, data privacy, and employment, and summarize business implications to leadership
• Develop and deliver training programs on legal compliance, contracting best practices, and risk management for business teams
• Assist with corporate transactions, equity matters, and board-related activities as needed

Corporate Counsel

Operations

• Become an expert at operating May vehicles — know how they work and how to drive them safely — while occasionally driving research vehicles for training purposes.
• Deliver standardized Autonomous Vehicle Operator training programs at site operations across the country, including classroom instruction and behind-the-wheel sessions.
• Conduct on-route ride-alongs, evaluations, and safety check-offs to certify new operators before they begin revenue service.
• Provide one-on-one coaching, feedback, and refresher training to reinforce safe operating procedures and customer service best practices for riders.
• Travel to new launch sites and existing site operations to support operator training, typically in 2-3 week deployments.
• Document trainee progress, certification results, and field observations and report back to the Field Operations Training Manager.
• Follow standardized training procedures and materials consistently across sites; flag improvement ideas to the Training Manager rather than altering programs in the field.

Field Operations Trainer

Program Manager

• Independently set timelines and goals for large scale Autonomous Vehicle development programs and/or other large scale initiatives that involve cross departmental collaboration
• Manage program timelines and track status of deliverables from the Product Definition through the Launch phases for one or multiple Autonomous Vehicle programs
• Identify dependencies across tasks and program teams to ensure continuous alignment
• Own the financial health of assigned platforms: establish budget baselines, forecast costs, and monitor expenditures throughout the program lifecycle, while identifying financial variances or risks and executing mitigation plans to optimize resource allocation
• Anticipate risks and work with Stakeholders to identify and execute mitigation plans
• Communicate with partners to align program plans to escalate vehicle issues
• Implement and manage change when necessary to meet desired outputs
• Present to stakeholders reports on progress as well as problems and solutions
• Champion prioritization of safety and quality
• Support Engineering leaders in continuous improvement of the vehicle development and program management process
• Develop and leverage relationships across functions to ensure clear communication and seamless follow-through

Lead Program Manager

Manager

• Referencing ISO 8800, ISO 21448 and AV industry best practices, develop the strategy for ensuring safe autonomous system development, deployment and maintenance.
• Set the strategy for how we systematically break down operational design domain components and driving behavior components and how these are validated in aggregate and on a per behavior level.
• Work with software, data science and systems engineering teams to ensure May Mobility safely trains new machine learning models to solve complex problems.
• Set the safety standard for how we prototype, test and deploy new AI solutions.Set the strategy for testing and validation of data sets and develop an assurance plan.
• Work with data science, systems engineering and software teams to set the strategy for how we establish safety launch targets (SPI- Safety Performance index) across vehicle behaviors and in aggregate.
• Create and confirm the detailed functional safety case according UL4600 and ISO26262 including a schedule to hit the company’s product launches
• Own and manage the safety plan and ensure that the schedule and required artifacts are met
• Train and coach the development team through every aspect of the safety plan through appropriate process and methods
• Communicate with all required stakeholders to identify and define technical/ system requirements, safety requirements tied to the safety goals.
• Ensure that functional safety confirmation measures such as audits and assessments are planned and complete.

Senior Manager, Autonomy Safety

• Lead and continuously improve the incident management process across all AV operational sites, ensuring consistent, high-quality response to incidents of all severities.
• Enable and support regulatory incident reporting, including NHTSA Standing General Order requirements and applicable state-level reporting, ensuring accurate and timely submissions.
• Develop and maintain incident response standards, escalation frameworks, and operational protocols in alignment with May Mobility's safety case and risk management approach.
• Design and lead a tiered incident response team structure that scales effectively with fleet growth, balancing full-time and contract resources to maintain coverage and quality.
• Serve as the primary point of contact for regulators, first responders, and partners during significant incidents, coordinating response and communications in partnership with Legal and Safety leadership.
• Work cross-functionally with Safety, Engineering, Legal, and Operations to ensure incidents drive systemic improvement, not just resolution.
• Develop and lead the talent within the incident management team, building a clear career pathway and ensuring the team has the skills and capacity to meet operational demands.
• Provide regular reporting to senior leadership on incident trends, team performance, and emerging risks.

Manager, AV Incident Management

• Manage program timelines and track status of deliverables from the Product Definition until the OK to Launch phase for one or multiple Autonomous Vehicle platforms
• Identify dependencies across tasks and program teams to ensure continuous alignment
• Track risk and manage mitigation plans
• Communicate with development partners and customers to align program plans to escalate vehicle issues
• Develop and manage program budgets, tracking expenditures across the full program lifecycle to ensure financial targets are met and variances are proactively addressed
• Lead cross-functional teams to define, track, and achieve bill of material cost targets in support of growth objectives
• Implement and manage change when necessary to meet desired outputs
• Present to stakeholders reports on progress as well as problems and solutions
• Champion prioritization of safety and quality
• Support continuous improvement of the vehicle development and program management process
• Develop and leverage relationships across functions to ensure clear communication and seamless follow-through

Senior Program Manager

Machine Learning Engineer

• Design, implement and own ML metrics and evaluation pipelines spanning offline model evaluation, simulation and on-road performance.
• Build and maintain test, regression and hillclimbing suites that gate model and stack releases, including automated triage of regressions to root cause.
• Drive model improvement through loss analysis, error mining, and data balancing/curation strategies for training and evaluation sets.

Machine Learning Engineer II – Autonomous Driving Performance Evaluation

• Deploy and Optimize Machine Learning model architectures across May’s Autonomous Driving training and inference stacks.
• Own the model-compilation and deployment pipeline end-to-end.
• Establish and defend latency/throughput budgets across the AV stack, including profiling, regression and integrity tests.

Machine Learning Engineer II – Autonomous Driving, Inference Runtime

Engineer

• Work independently with cross-functional teams to develop software and system requirements.
• Lead major architectural updates to the perception stack, including system-level design, modularization, and migration strategies that improve scalability, latency, and maintainability across the fleet.
• Design, develop, and own scene detection and activation capabilities, classifying the operating scene/scenario and triggering the appropriate perception behaviors and operational design domain (ODD) logic at runtime.
• Improve perception robustness under degradation and adverse weather, including sensor fault detection, health monitoring, graceful degradation, and fallback strategies for rain, snow, fog, glare, low-light, and sensor soiling/occlusion conditions.
• Design, implement, and test state-of-the-art perception features on time with high quality, industrial-grade production code.
• Integrate large-scale multi-modal models (including but not limited to VLMs and LLMs) into the perception stack to improve semantic scene understanding and reasoning.
• Track and trend technical performance of perception in the field.
• Lead major feature development including feature design, code reviews, issue diagnosis, and resolution.
• Lead extensive testing to validate features and satisfy release schedules.
• Lead development related to data, development, and ML pipelines, specifically focused on multimodal data alignment for training foundation models.

Lead ML/Perception Engineer

Data Scientist

• Set and own the data science strategy across simulation and synthetic data, ML evaluation (perception, prediction, planning), fleet operations analytics, and the data platform that supports them; translate that strategy into a 12–24 month roadmap with measurable milestones.
• Lead, grow, and develop a team of senior data scientists, ML engineers, and front-line managers; recruit from a small expert pool, calibrate the bar, and build a hiring brand that allows May Mobility to win against AV, robotics, and AI competitors.
• Partner with Engineering, Product, Safety, and Operations leaders to define release criteria, performance metrics, and ODD-expansion gates; use data to make the business case for what we deploy, where, and when.
• Drive ML and analytics applications end-to-end: dataset curation, scenario coverage, modeling, offboard evaluation, productionization, and continuous monitoring of fleet performance in the wild.
• Establish measurement and experimentation standards across the company — including before/after analyses for stack changes, A/B-style comparisons in simulation, and statistically credible reporting on real-world incidents.
• Lead team-wide quality activities including design and code reviews; hold the bar on engineering rigor for production data science systems.
• Track and trend technical performance of the autonomy stack in the field; surface root causes, prioritize fixes with engineering, and represent fleet-data findings to executives, regulators, and partners.
• Provide technical guidance to Engineering and Operations leaders on issue diagnosis, resolution, and the ML changes most likely to move our key safety and service metrics.
• Represent May Mobility's data science work externally where appropriate — through publications, conference talks, partner reviews, and recruiting.