Senior Software Engineer - ML/AI (Remote)

As a Senior Software Engineer - ML/AI at BrightSign, your responsibilities will include providing technical leadership on ML/AI technologies, defining and evolving software architecture for ML capabilities, collaborating with cross-functional teams to deliver new features, writing efficient code, ensuring software quality, and staying updated on the latest trends in ML/AI. You'll play a critical role in shaping our innovative digital signage solutions.

To qualify for the Senior Software Engineer - ML/AI role at BrightSign, candidates should have a minimum of 7 years of experience in embedded software development, with at least 2 years specifically focused on ML/AI applications. Proficiency in C++ and Python is vital, along with a strong understanding of ML inference technologies and embedded Linux. A BA/BS in a relevant field is preferred along with leadership experience in agile environments.

BrightSign supports ML/AI feature development by utilizing its advanced media players equipped with Neural Processing Units (NPUs). This technology allows for efficient inference operations, enabling developers to create innovative features that enhance the user experience. The engineering team collaborates closely to define requirements, ensuring that software architecture is adaptable to support these complex needs.

Yes! BrightSign offers the flexibility of remote work for the Senior Software Engineer - ML/AI role. While you can work from the comfort of your home, it's ideal for candidates situated within the US or Western Europe time zones to facilitate smooth collaboration with various teams across our global operations.

A Senior Software Engineer - ML/AI at BrightSign will work with technologies such as C++, Python, Typescript/Javascript, and embedded Linux. Familiarity with machine learning inference, especially in areas like machine vision and speech technologies, is critical. Knowledge of cloud technologies and ML Ops will also be beneficial in leveraging our full tech stack.

In answering this question, highlight specific projects where you've implemented ML/AI solutions on embedded Linux platforms. Discuss the challenges you faced and how you overcame them, the tools you used like OpenEmbedded, and any performance optimizations you successfully made.

Illustrate your leadership skills by detailing a project where you spearheaded the development of an ML/AI feature. Discuss your role, the team dynamics, how you managed the project timeline, and the innovative solutions you provided.

Focus on your approach to quality assurance, including automated testing, code reviews, and setting up continuous integration. Mention specific tools and processes you've implemented to maintain high standards in ML/AI software quality.

Share your insights on identifying performance bottlenecks. Discuss techniques you've applied to optimize resource usage, enhance speed, and ensure responsiveness, especially in resource-constrained environments that are typical of embedded systems.

Discuss your grasp of ML Ops concepts and practices, focusing on how they improve collaboration between development and operations teams. Mention any relevant experiences you've had in implementing ML Ops methodologies to streamline model deployment and monitoring.

Illustrate your communication skills and teamwork by sharing experiences of collaborating with product management, design, and QA teams. Discuss specific instances of how you've successfully navigated technical discussions with non-technical stakeholders.

Outline the cloud platforms you have experience with and how you've integrated them with ML/AI applications. Highlight specific projects where cloud computing played a crucial role in delivering scalable ML solutions.

Explain your methods for continuous learning, such as attending conferences, following industry experts, and engaging with technical communities. Mention any courses or certifications that have contributed to your knowledge base in ML/AI.

Discuss your commitment to writing clean, maintainable code. Outline the specific coding standards and best practices you adhere to, and how you ensure compliance to improve code clarity and team collaboration.

Describe the specific bug and the impact it had on the project. Highlight your systematic approach to debugging, the resources you utilized, and finally, how you solved the problem, ensuring software stability and performance.