Senior Autonomy 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 an Autonomy Software Engineer, you will help build and maintain the systems, pipelines, and tooling that let our perception engineers build, test, deploy, and improve how our vehicles see and interpret the world. This is a hands-on, software-engineering-focused role with three areas of contribution: building and maintaining the CI/CD pipelines, developer tooling, and infrastructure the perception stack depends on; supporting and improving the inference pipeline so models run reliably within real-time latency budgets on the vehicle; and developing and improving heuristic (classical, geometric, and rule-based) perception methods that complement learned models. You will work closely with senior engineers, write high-quality production code, and grow into owning larger pieces of the stack over time.

• →Work independently with cross-functional teams to develop software, CI/CD pipelines, and system requirements.
• →Build and maintain CI/CD pipelines for the perception stack — automated builds, unit/integration/regression testing, performance checks, and deployment workflows.
• →Develop and maintain developer tooling and infrastructure, including build scripts, test harnesses, log/replay and simulation tooling, data tooling, and debugging/visualization utilities.
• →Contribute to the inference pipeline: model integration, runtime tuning, latency/throughput measurement, and deployment of models to on-vehicle compute, with guidance from senior engineers.
• →Develop, tune, and improve heuristic perception methods (geometric reasoning, signal processing, filtering, and rule-based logic) for detection, tracking, and scene understanding, and as deterministic fallbacks.
• →Help build and maintain tooling to track and trend perception performance in the field, including metrics collection and regression monitoring.
• →Write clear, maintainable, industrial-grade production code and participate in code reviews.
• →Diagnose and resolve issues, and write tests to validate features and support release schedules.
• →Support data and ML pipelines that feed model training, evaluation, and deployment.

Success in this role typically requires the following competencies:

• Solid software engineering fundamentals: data structures, algorithms, version control, debugging, and writing clean, testable code.
• Familiarity with CI/CD concepts, build systems, and automated testing, and eagerness to deepen these skills.
• Interest in and exposure to real-time systems, inference pipelines, or runtime performance considerations.
• Familiarity with heuristic / classical perception or estimation concepts (e.g., geometry, filtering, tracking) is a plus.
• Basic understanding of the ML development cycle and how models are deployed (depth in ML is not required).
• Working understanding of data: how data pipelines feed training and evaluation.
• Good attention to detail and a habit of testing your own work.
• Strong written and verbal communication skills and willingness to collaborate and learn.

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%

• 1–3 years of professional software engineering experience (internships, co-ops, and significant project work count).
• Bachelor's degree in Computer Science, Computer Engineering, Robotics, or a related field that requires a strong engineering and/or mathematical foundation — or equivalent practical experience.
• Programming skills in C++ and/or Python; comfort working in a Linux environment.
• Experience with version control (e.g., Git) and an understanding of testing and CI/CD basics.
• Demonstrated ability to write and debug software through coursework, internships, personal projects, or prior roles.

• Exposure to CI/CD and build tooling (e.g., GitHub Actions/GitLab CI/Jenkins, CMake, Bazel) and containerization (Docker).
• Exposure to real-time, robotics, or autonomy software, or to inference/runtime performance work.
• Familiarity with heuristic / classical perception, computer vision, multi-target tracking, or sensor fusion concepts.
• Familiarity with ML/DL frameworks (e.g., PyTorch/TensorFlow) at a basic level.
• Exposure to simulation, log-replay, or data-pipeline tooling.
• Interest in safety-critical systems and graceful-degradation strategies.
• Personal projects, open-source contributions, or coursework that demonstrate strong software engineering fundamentals.