Lead ML/Perception Engineer

May Mobility is entering an exciting phase of growth as we expand our first-of-its-kind autonomous shuttle and mobility services across the nation. Launched in 2017 with a strong team of experienced roboticists and software engineers with decades of experience fielding robotic systems in the wild, May Mobility is looking to expand its team of robotics engineers with a background in robotics or autonomous vehicles.

As Perception Lead, you will own the technical direction of how our vehicles see and interpret the world. This is a hands-on role focused on three priorities: advancing scene detection so the system reliably recognizes its operating context and engages the right behavior; hardening perception against sensor degradation and adverse weather (rain, snow, fog, glare, low light, and sensor soiling or occlusion) so performance degrades gracefully and safely; and driving major architectural updates to evolve our perception stack into a scalable, maintainable, production-grade foundation. You will set the technical bar, mentor engineers, and deliver capabilities that operate safely in the real world.

• →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.

Success in this role typically requires the following competencies:

• Proven ability to architect and evolve large-scale perception or autonomy systems, balancing performance, latency, safety, and long-term maintainability.
• Strong grasp of perception robustness and reliability engineering, including degraded-sensor operation, adverse-weather handling, fault detection, and graceful degradation.
• Experience with scene/scenario understanding and runtime activation logic (e.g., context classification, ODD monitoring, behavior triggering).
• Familiar with ML development cycle, deployment, and optimization.
• Deep understanding of data: data pipeline, data balancing, data mining, and data-driven performance improvement.
• Knowledge of multimodal learning techniques, including contrastive learning and prompt engineering for zero-shot visual recognition.
• Deep understanding of testing frameworks and workflows.
• Excellent attention to detail and rigorous testing methodology.
• Exceptional written and verbal communication skills and team-leading abilities.

Candidates most successful in this role typically hold the following qualifications or comparable knowledge or experience:

• Standard office working conditions which includes but is not limited to:
• Prolonged sitting
• Prolonged standing
• Prolonged computer use
• Travel required? -  Moderate: 11%-25%

Want to learn more about our culture & benefits? Check out our website!

May Mobility is an equal opportunity employer.  All applicants for employment will be considered without regard to race, color, religion, sex, national origin, age, disability, sexual orientation, gender identity or expression, veteran status, genetics or any other legally protected basis.   Below, you have the opportunity to share your preferred gender pronouns, gender, ethnicity, and veteran status with May Mobility to help us identify areas of improvement in our hiring and recruitment processes. Completion of these questions is entirely voluntary.  Any information you choose to provide will be kept confidential, and will not impact the hiring decision in any way. If you believe that you will need any type of accommodation, please let us know.

Note to Recruitment Agencies: May Mobility does not accept unsolicited agency resumes. Furthermore, May Mobility does not pay placement fees for candidates submitted by any agency other than its approved partners.

• A minimum of 5+ years of industry experience working on real-world robot systems maintaining high-quality industrial-grade code.
• Master's degree in Robotics, Computer Science, or Computer Engineering, or a field that requires a strong mathematical and/or engineering foundation.
• Demonstrated experience leading or making major architectural decisions for a perception, robotics, or autonomy software stack.
• Strong programming skills in C/C++/Python; software development in Linux environments.
• Strong experience in ML/DL development with PyTorch/TensorFlow.
• Direct experience developing or fine-tuning large-scale multi-modal models (e.g., multi-sensor fusion models, foundation models, including VLM backbones) for real-world perception applications.
• Experience working on a combination of several of the following real-time areas:
• Computer Vision: object detection, classification, segmentation.
• Semantic scene understanding, scene/scenario detection, and open-vocabulary detection.
• Perception robustness in degraded or adverse-weather conditions.
• Multi-target tracking and sensor fusion.
• Extensive experience deploying features/ML/DL models in real-time systems with high accuracy and low latency.

• Experience developing all-weather / adverse-condition perception, sensor degradation modeling, fault detection, and graceful-degradation strategies for safety-critical systems.
• Experience with scene/scenario classification, ODD monitoring, and runtime capability activation in autonomous systems.
• Experience re-architecting or migrating large, legacy perception systems to modern, scalable frameworks.
• Experience utilizing large-scale multi-modal models for edge-case (long-tail) detection and explainable AI in autonomous systems.
• Familiar with synthetic data in ML system development.
• Familiar with Reinforcement Learning (RL) for ML systems.
• Familiar with ML/DL optimization on real-time products with limited compute resources (e.g., quantization of large transformer models).
• Strong background demonstrated through high-quality capability deliveries to robots working in the field.