Staff Software Engineer - Deterministic Machine Learning

As a Staff Software Engineer specializing in Deterministic Machine Learning at MicroVision, your responsibilities include designing, developing, and implementing machine learning algorithms that are mathematically or statistically verifiable. You will ensure model accuracy and consistency, troubleshoot programming issues, and work closely with cross-functional teams to integrate algorithms into our Automotive LiDAR products.

To qualify for the Staff Software Engineer - Deterministic Machine Learning position at MicroVision, you should have a Master’s degree in a related science field or a Ph.D. with relevant experience. You must also have a strong foundation in software engineering, digital signal processing, and practical knowledge of linear algebra and deep learning. Additionally, excellent communication skills are essential for collaboration with various technical teams.

At MicroVision, the Staff Software Engineer - Deterministic Machine Learning follows best practices in software engineering according to ASPICE and ISO26262 standards. This ensures that our processes are compliant, quality-driven, and market-ready, all while enhancing the performance and reliability of our Automotive LiDAR products.

The work environment at MicroVision for the Staff Software Engineer - Deterministic Machine Learning is dynamic and collaborative. You will be engaging with multiple teams to tackle challenges and innovate solutions. We're proud of our flexible work schedule that accommodates both on-site and remote work, enhancing work-life balance while maintaining team connectivity.

While experience in the ADAS/Automotive Machine Learning industry is helpful for the Staff Software Engineer role at MicroVision, it is not a strict requirement. The focus is on verifying your expertise in deterministic algorithms and your ability to implement high-quality software solutions, regardless of specific industry experience.

To ensure accuracy and consistency of machine learning algorithms, I would implement thorough testing practices, including validation on diverse datasets, careful statistical analysis, and continuous monitoring of the algorithm's performance in real-world situations. Regularly updating the algorithms based on feedback and findings is also critical.

I have extensive experience with deterministic machine learning algorithms, particularly focusing on statistical proofs of performance. I’ve designed, developed, and refined algorithms that consistently produce reliable results, ensuring they meet the necessary accuracy metrics before deployment.

I approach cross-functional team collaboration by fostering open communication, encouraging feedback, and utilizing project management tools to track progress. I believe that clear objectives and shared goals are pivotal for successful teamwork in developing sophisticated products like automotive LiDAR systems.

I am well-versed in ASPICE and ISO26262 standards, which guide software engineering practices in the automotive sector. I have implemented these frameworks in previous projects to ensure compliance and enhance product safety and reliability, crucial for developing advanced driver assistance systems.

Digital signal processing is vital in my work as it underpins the processing of signals to extract meaningful features essential for machine learning models. My understanding of DSP principles allows me to implement effective algorithms that facilitate accurate data interpretation and model performance.

I developed a complex algorithm aimed at minimizing latency in real-time machine learning applications. The challenge lay in balancing accuracy with speed. I approached it by conducting thorough research, utilizing optimization techniques, and periodically testing against benchmark datasets to refine the algorithm's performance.

For model validation, I utilize cross-validation techniques, A/B testing, and holdout datasets to assess model performance. I ensure the models are not overfitted by analyzing their behavior on unseen data, which allows me to validate their generalizability.

I stay current with advancements in machine learning by reading academic journals, attending conferences and webinars, and participating in relevant professional communities. Engaging with peers and experts also helps me to keep abreast of the latest technologies and methodologies.

I have substantial experience implementing linear algebra concepts in machine learning, as they form the foundation of many algorithms. I utilize matrix operations for efficient computations and transformations of data, optimizing performance and scalability of my models.

When facing setbacks in algorithm development, I adopt a methodical approach to analyze the issues. I collaborate with team members for new insights, revisit prior models and data, and pivot our strategies as needed, using setbacks as learning opportunities to enhance the final product.