Staff Machine Learning Software Engineer, Perception

A Staff Machine Learning Software Engineer at Cruise is tasked with driving the technical roadmap for perception evaluation systems, designing scalable ML evaluation frameworks, and establishing quality standards across various projects. This role involves significant collaboration with ML researchers, ensuring a successful integration of advanced technology solutions in autonomous vehicles.

To qualify for the Staff Machine Learning Software Engineer position at Cruise, candidates should possess a BS, MS, or Ph.D. in Computer Science or related fields along with at least 5 years of software engineering experience. Candidates must also have 2 or more years of technical leadership, along with expert-level programming skills in Python and C++.

Cruise takes pride in fostering a diverse, equitable, and inclusive environment. The company welcomes applicants from various backgrounds and identities, ensuring that every employee's unique experience is valued. This commitment plays a vital role in creating a supportive workplace culture where innovation and collaboration thrive.

The salary range for the Staff Machine Learning Software Engineer position at Cruise varies between $175,100 to $257,500, depending on several factors such as location, skills, and experience. Additionally, employees are often offered bonuses and comprehensive benefits as part of their compensation package.

Candidates for the Staff Machine Learning Software Engineer position at Cruise should be proficient in Python and C++. Familiarity with machine learning frameworks like PyTorch and TensorFlow is highly beneficial, along with hands-on experience in building distributed systems and large-scale data processing pipelines.

It’s essential to detail your hands-on experience with ML frameworks like TensorFlow and PyTorch. Highlight specific projects where you utilized these frameworks to improve perception systems, discussing the challenges faced and how you overcame them.

Discuss the importance of code reviews, testing practices, and documenting code standards. Emphasize how maintaining clear communication with team members can lead to enhanced collaboration and improved overall project outcomes.

Provide a real example that showcases your problem-solving skills. Explain the context, your thought process, the action taken, and the results achieved. This demonstrates your ability to navigate challenges systematically.

Talk about the significance of defining clear performance metrics based on model objectives, and how you would collaborate with cross-functional teams to ensure alignment on these evaluation metrics, focusing on both quality and usability.

Detail your familiarity with designing scalable distributed systems. Discuss your understanding of components like data storage, load balancing, and fault tolerance, along with specific examples where you contributed to or led such projects.

Emphasize the necessity of thorough technical documentation for ensuring knowledge transfer, onboarding new team members, and maintaining project clarity. Provide examples of how proper documentation has positively impacted team performance in your past roles.

Describe a systematic approach to receiving and integrating feedback, emphasizing how such practices improve both personal and team performance. Share examples where you utilized feedback to enhance project outcomes.

Discuss potential issues such as data quality, latency concerns, or maintaining model performance over time. Highlight strategies you have employed to address these challenges, demonstrating your proactive approach to continuous improvement.

Describe your experience working alongside different teams, like Data or Infrastructure. Discuss how you facilitated communication and alignment on project goals to achieve successful outcomes.

Share your passion for technology and innovation within the autonomous vehicle space, emphasizing how these advancements can shape transportation, reduce accidents, and improve urban planning, fostering an inspiring conversation about the future of mobility.