Canopy

Engineering Manager

• Build, mentor, and lead a high-performing team of Quality Engineers and Test Engineers. Provide technical guidance, set performance goals, and foster a culture of accountability and precision. Allocate resources effectively to support multiple concurrent product programs.
• Lead the end-to-end hardware testing strategy to ensure all products meet functional, performance, environmental, safety, and regulatory compliance standards. Oversee the design and build-out of internal testing capabilities, including automated test rigs and validation processes, to ensure rigorous coverage prior to mass production.
• Manage the hardware testing facilities and vehicle fleet to ensure the team is fully equipped to execute their work. This includes overseeing the procurement and maintenance of bench equipment (e.g., thermal chambers, oscilloscopes, data acquisition), triaging workbenches, and test vehicles. You will manage the asset utilization to ensure the wider Canopy team has the necessary tools to identify failure modes and validate fixes without bottlenecks.
• Champion the "Design for Quality" methodology within the engineering organization. Lead key quality processes including DFMEA (Design Failure Mode and Effect Analysis) and PFMEA (Process Failure Mode and Effect Analysis). Facilitate 8D problem-solving workshops and ensure a closed-loop "lessons learned" approach is applied to every design iteration.
• Drive manufacturing quality standards with our Contract Manufacturers (CMs) and key suppliers. Define technical quality requirements for mass production, conduct supplier audits, and oversee the implementation of control plans to ensure consistent product quality.
• Lead the Advanced Product Quality Planning (APQP) framework with suppliers and manage the Production Part Approval Process (PPAP) to ensure components and assemblies meet stringent automotive standards before mass production.
• Own the warranty return and RMA process. Lead the triage of field failures to identify root causes (RCA) and drive corrective actions for current and future products. Maintain and update core engineering documentation to prevent the recurrence of known failure modes, directly influencing the product roadmap based on field data.
• Establish and maintain the Hardware Quality Management System, ensuring alignment with relevant automotive standards (e.g., IATF 16949, ISO 9001). Manage external relationships with certification bodies and test labs to ensure timely product certifications (FCC, CE, PTCRB, etc.).

Engineering Manager, Hardware Quality

Machine Learning Engineer

• Use machine learning techniques to train, debug, and evaluate models for customer deliveries ranging from quick prototypes to full production-level models.
• Perform exploratory data analysis on the large sensory datasets (image, audio, radar, accelerometer) we have gathered, to develop greater understanding of the problem domain.
• Define and improve best practices of ML training, systems development, testing and evaluation.
• When needed, carry out data collection campaigns using custom tooling for capture and labelling.
• Work closely with Data and MLOps engineers, and Quality Assurance to improve the quality of our datasets and pipelines.
• Work with product managers to help integrate the machine learning solutions and deliver on the desired user experience.

• Contribute to the design, development, and deployment of robust machine learning models for production use in real-world security applications.
• Develop within the full machine learning lifecycle; from problem definition to data pipeline design, model development, validation, deployment, and monitoring.
• Establish and refine best practices in our ML system architecture, CI/CD pipelines for ML, and reproducible research methodologies.
• Collaborate with cross-functional stakeholders including product managers, data engineers, and MLOps teams to ensure seamless model integration and delivery.
• Perform advanced exploratory data analysis on large-scale sensory datasets (image, audio, radar, accelerometer) to derive insights and guide modeling strategies.
• Stay ahead of industry advancements in machine learning, AI sensing, and signal processing, incorporating the latest innovations into Canopy’s technology stack.
• Mentor and guide junior engineers and contribute to the hiring process and technical reviews.

Senior Machine Learning Engineer

Technical Program Manager

• Lead the end-to-end software release lifecycle across firmware, app, cloud, platform services, and external partner systems ensuring compatibility, reliability, high-quality, and well-communicated rollouts.
• Drive product quality and release reliability by defining and enforcing release criteria, gates, approval processes, and coordinating cross-team testing and readiness.
• Create, track, and analyze key KPIs including pre- and post-deployment metrics to monitor release performance, identify trends, and inform continuous improvement.
• Manage on-call and incident processes, including escalations, SLAs, and post-incident reviews, while coordinating with all Customer Care tiers for effective resolution.
• Monitor release health in real time and lead rapid triage and rollback/mitigation decisions when necessary.
• Maintain transparent communication on release status, risks, and outcomes to leadership and stakeholders.
• Continuously improve release, support, and incident processes through documentation, change logs, release notes, release process automation, and operational excellence initiatives.

Technical Program Manager, Quality and Release

Full-stack Engineer

• Design, develop, and deploy cloud-based solutions and services, leveraging industry best practices and cloud technologies.
• Collaborate with product management and other stakeholders to define requirements and translate them into scalable and reliable cloud solutions.
• Architect and optimize cloud infrastructure, ensuring scalability, performance, and security.
• Develop and implement cloud-based software applications, APIs, and microservices using modern programming languages and frameworks.
• Implement and automate cloud deployment, monitoring, and management processes using Infrastructure-as-Code (IaC) and DevOps principles.
• Collaborate with cross-functional teams, including software engineers, data engineers, and operations, to ensure seamless integration of cloud services and components.
• Monitor and optimize cloud resources, performance, and costs, identifying and implementing efficiency improvements.
• Stay up to date with cloud technologies, industry trends, and emerging best practices, evaluating and recommending new tools and technologies to enhance our cloud capabilities.
• Ensure compliance with security and data privacy standards and regulations in all cloud-based solutions and services.
• Mentor and provide technical guidance to junior engineers, fostering a culture of knowledge sharing and continuous learning.
• Collaborate with the development team to conduct code reviews, provide feedback, and ensure adherence to coding standards and best practices.
• Participate in agile development processes, including sprint planning, stand-ups, and retrospectives.

Engineering Manager, Hardware Quality

Machine Learning Engineer

Senior Machine Learning Engineer

Technical Program Manager, Quality and Release

Senior Engineer, Platform Core Services