Senior Software Engineer, ML Data Pipeline (Distributed Training)

As a Senior Software Engineer specializing in ML Data Pipeline at Latitude AI, your main responsibilities will include collaborating with perception experts to build scalable machine learning solutions, architecting state-of-the-art implementations, and developing robust cloud services for data management and workflow. You will also focus on building production-quality software that adheres to strict safety standards and optimizing machine learning training pipelines.

Candidates for the Senior Software Engineer position at Latitude AI need to have at least a Bachelor's degree in computer engineering, computer science, electrical engineering, robotics, or a related field. You'll also need four or more years of relevant experience, or two or more years if you have a Master’s or PhD. Proficiency in C++ or Python software development, coupled with a successful track record in product development, is essential.

At Latitude AI, Senior Software Engineers work on groundbreaking projects focused on automated driving technology. You will build machine learning capabilities for Level 3 driving, work on scalable robotics solutions, and contribute to the cloud infrastructure for validation and safety processes. You’ll be at the forefront of transforming how automated vehicles interact with their environments.

Latitude AI offers a competitive compensation package for its Senior Software Engineers, including comprehensive health insurance, a 401(k) retirement plan with employer matching, and unlimited vacation. Additional perks include wellness stipends, professional development reimbursement, and team bonding activities, all aimed at fostering a positive and supportive work environment.

Yes, Latitude AI does offer visa sponsorship for the Senior Software Engineer position. Candidates must be legally authorized to work permanently in the United States and will need to verify their employment eligibility at the time of hire.

When discussing your experience with machine learning frameworks in the interview, be sure to highlight the specific frameworks you've worked with, such as TensorFlow or PyTorch. Share examples of projects where you utilized these frameworks to implement models, discussing the challenges you faced and how you overcame them to optimize performance.

In your response, outline your understanding of distributed systems, emphasizing best practices like maintaining robust communication between services, ensuring data consistency, and effectively handling failures. Share any relevant experiences where you contributed to the architecture, design, or implementation of a distributed software solution.

When addressing this question, choose a project that showcases your problem-solving skills and technical expertise. Discuss the project scope, your role, the challenges encountered, and the successful outcome. Focus on what you learned from the experience and how it has shaped your approach to future projects.

Share your strategies for effective task prioritization, which might include using agile methodologies, collaborating with stakeholders for feedback, and breaking down large tasks into manageable segments. Emphasize your ability to adapt to shifting priorities while maintaining focus on project deadlines.

Discuss your commitment to code quality by mentioning practices like code reviews, unit testing, and continuous integration. Provide examples of how you've implemented these practices in past projects, highlighting how they contributed to a reduction in bugs and improved project reliability.

Express your dedication to continuous learning by mentioning resources such as online courses, webinars, and relevant communities. Discuss specific instances of how you've applied new knowledge in your work or how you anticipate future trends in the field.

Illustrate your cloud services experience by discussing the platforms you've used, such as AWS, Azure, or Google Cloud. Share how you've leveraged these services to build and deploy machine learning solutions, focusing on the advantages they provided in terms of scalability and efficiency.

Emphasize the importance of open communication and respect for each team member's expertise. Share examples of successful collaborations in previous roles where you worked closely with those in differing fields, and how those experiences enhanced project outcomes and fostered a positive team environment.

Discuss the methodologies and frameworks you apply to assess safety and reliability, such as conducting thorough testing and validation processes. Highlight any hands-on experience you have with safety-critical systems as well as your understanding of relevant industry standards.

Pick a specific incident where you had to dive deep into complex software issues. Describe your methodical troubleshooting approach, such as isolating variables, examining logs, and testing hypotheses, and explain the impact of your actions on the project's progress.