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Description:
Stack is focused on AI advancements across diverse technical domains, transforming how AI is applied in the physical realm.
The company aims to navigate and understand the world with unprecedented safety, reliability, and efficiency, impacting modern industries.
The Senior Staff Software Engineer, Machine Learning will develop state-of-the-art deep learning/machine learning models to enhance perception solutions in challenging driving scenarios.
The ideal candidate will possess a strong mathematical background, extensive research experience, and a practical, product-focused mindset to create highly performant safety-critical perception solutions.
Responsibilities include researching and developing deep learning/machine learning approaches for self-driving systems, owning the delivery of perception modules, guiding software development from prototype to production, and deploying models in resource-constrained environments.
Requirements:
Candidates must have extensive experience in Computer Vision, Machine Learning, Deep Learning, or other relevant areas of Artificial Intelligence, demonstrated through industry experience or a publication record.
A proven track record of driving deep learning applied research projects from conception to completion is required, including problem definition, experimentation, iteration, and publication or productization.
Excellent cross-team collaboration skills are necessary.
Candidates should have extensive experience with deep learning frameworks such as PyTorch or TensorFlow.
Strong software engineering skills in Python and/or C++ are essential.
Benefits:
Stack is committed to being an equal opportunity workplace, fostering a culture of inclusion, entrepreneurship, and innovation across diverse backgrounds.
The company values diverse teams, believing they produce the best ideas and outcomes.
Candidates should be aware that employment may be contingent upon verification of residence, U.S. person status, and/or citizenship status due to national security-related requirements.