Staff Machine Learning Engineer

About the role

You will oversee the integration and deployment of complex perception models within Lucid's production ADAS and autonomous driving software. This role requires you to own the full lifecycle of experimental perception components, translating cutting-edge research into robust, high-performance software that meets stringent automotive safety standards. You will be responsible for ensuring that these systems operate reliably on automotive-grade hardware under real-world driving conditions. A significant part of your work will involve collaborating across disciplines to align machine learning capabilities with vehicle-level requirements and customer expectations. You will design and maintain production-grade pipelines that enable continuous improvement and safe deployment of perception features. This position is critical in bridging the gap between algorithmic innovation and functional safety in a production vehicle environment. Your contributions will directly influence the reliability and capability of Lucid's advanced driver-assistance systems.

Key facts

What you'll do

• Integrate camera and LiDAR perception models into the core Lucid software architecture using standardized interfaces and modular design principles.
• Convert experimental components into production-ready modules that include comprehensive diagnostic tools, health monitoring, and task scheduling mechanisms.
• Optimize inference pipelines by applying mixed-precision techniques, CUDA kernels, and TensorRT optimizations to meet real-time execution constraints on embedded platforms.
• Develop multithreaded scheduling frameworks and containerized deployment solutions that ensure deterministic behavior across diverse vehicle hardware configurations.
• Create and manage end-to-end CI/CD pipelines that support nightly builds, hardware-in-the-loop verification, and key performance indicator tracking for perception systems.
• Automate regression testing procedures, data recording workflows, and evaluation frameworks to ensure consistent validation of model behavior over time.
• Partner closely with hardware engineers, software developers, and machine learning researchers to deliver cohesive SDKs and customer-facing perception features.
• Build runtime performance monitoring dashboards, error reporting mechanisms, and detailed logging systems for the entire perception stack to aid in rapid troubleshooting.
• Implement data management strategies that support efficient handling of large-scale sensor inputs while maintaining system responsiveness and storage efficiency.
• Define and maintain technical documentation for perception modules, deployment procedures, and integration guidelines for both internal and external consumers.
• Establish benchmarks and profiling tools to measure throughput, latency, and accuracy of perception models in various operational design domains.
• Drive the adoption of best practices in software engineering, machine learning operations, and automotive functional safety throughout the perception team.

Requirements

• Hold a Bachelor of Science or Master of Science degree in Electrical Engineering, Computer Science, or a closely related technical discipline from an accredited institution.
• Possess a minimum of 7 years of professional experience focused on software engineering for autonomous systems or computer vision perception tasks in automotive or similar safety-critical environments.
• Demonstrate advanced proficiency in both Python and C++ with a strong understanding of object-oriented design, memory management, and performance optimization.
• Have hands-on experience with GPU acceleration technologies, OpenCV libraries, and Robot Operating System versions 1 and 2 for perception and control applications.
• Show a proven track record of developing and deploying software components that comply with automotive industry standards and functional safety considerations.
• Exhibit strong problem-solving skills when dealing with real-time constraints, sensor fusion challenges, and integration complexities in embedded systems.
• Display excellent communication abilities to collaborate effectively with cross-functional teams including mechanical engineers, system architects, and validation specialists.
• Maintain a disciplined approach to version control, code review, and testing methodologies to ensure high-quality software delivery.

Nice to have

Preferred candidates will have experience with production machine learning systems in automotive or robotics domains. Knowledge of functional safety standards relevant to autonomous driving systems is highly valued. Experience with container orchestration platforms and cloud infrastructure is also considered beneficial for this role. Familiarity with model optimization frameworks and edge deployment strategies can provide additional value to the team.

Practical notes

This is a full-time position based in Newark, Canada. The role may involve occasional travel to support vehicle testing and validation activities as required by project milestones. Employment eligibility requirements and visa sponsorship considerations will be addressed during the hiring process as needed. Applicants should be prepared to meet the outlined experience and technical competencies during the evaluation process.