Senior Applied Scientist - Multimodal LLMs

As a Senior Applied Scientist at Samsara, your primary responsibilities include building and improving machine learning models, particularly multimodal models. You'll leverage our vast arrays of petabyte-scale data from various sensors and work closely with engineering teams to develop innovative AI products. Your role will also involve applying cutting-edge technologies from research, collaborating with cross-functional partners, and refining models using customer feedback.

To qualify for the Senior Applied Scientist position at Samsara, candidates should have over 5 years of experience in a relevant field such as applied science or machine learning engineering. A BS or MS in Computer Science or a related quantitative discipline is required, along with proficiency in programming languages like Python, Java, or C++. Familiarity with ML frameworks such as PyTorch or TensorFlow and strong data querying skills are also essential.

Samsara promotes a collaborative environment for Senior Applied Scientists through a combination of independent tasks and teamwork. You will work with various engineering and cross-functional teams, engaging in customer interviews and using analytics to enhance outcomes. This culture emphasizes teamwork, a growth mindset, and inclusivity, ensuring that your ideas and contributions are valued while pursuing common goals.

In the role of Senior Applied Scientist at Samsara, you'll work with a diverse range of data types, particularly from petabyte-scale sets gathered from sensors including video, GPS, audio, and more. The ability to analyze and model these various modalities will be crucial in delivering impactful machine learning solutions that enhance the safety and efficiency of operations across industries.

Samsara offers a competitive compensation package for the Senior Applied Scientist role, which includes an annual base salary ranging from $132,600 to $171,600 CAD. This compensation structure reflects the role's responsibilities, candidate qualifications, and geographical considerations. Employees may also receive bonuses and stock options as part of their total compensation.

When answering this question, clearly outline your relevant experience, including specific projects you’ve worked on. Discuss the types of machine learning models you have built, the tools and frameworks employed (such as PyTorch or TensorFlow), and the impact those models had. Highlight your problem-solving strategies and how feedback loops improved your work.

To answer this question effectively, share examples of previous experiences where you successfully collaborated with cross-functional teams. Emphasize your communication skills, your ability to align on project goals, and how you adapt to feedback from different stakeholders. This showcases your team-oriented approach and leadership capabilities.

Explain your approach to optimizing models, focusing on techniques like hyperparameter tuning, model retraining, and leveraging insights from customer feedback. Describe specific instances where these strategies led to measurable improvements, and if possible, mention any tools or frameworks you utilized in the optimization process.

Use the STAR method (Situation, Task, Action, Result) to provide a structured answer. Detail the challenge, your specific role in addressing it, the steps you took, and the successful outcomes. This demonstrates your analytical thinking and persistence in problem-solving.

Express your commitment to lifelong learning. Mention specific resources such as academic papers, podcasts, webinars, or online courses that you engage with regularly. Highlight any industry conferences you attend or communities you participate in, showcasing your proactive approach to staying current in the rapidly evolving field of AI.

Talk about your experience working with big data tools such as Spark or SQL, emphasizing how you've processed large datasets. Provide examples of specific projects where you utilized these technologies to derive insights or improve performance, illustrating your capability to work at scale.

Your answer should include methodologies you apply for validation and testing, such as cross-validation, A/B testing, or performance metrics. Discuss how you ensure robustness, accuracy, and reliability in your models, and highlight the importance of these practices in a production environment.

Be honest and use this question as an opportunity to show your growth mindset. Discuss how you received the feedback positively, the steps you took to improve your work based on that feedback, and the successful outcomes that resulted from your efforts. This reflects your openness to learning and adaptability.

Discuss how you incorporate customer feedback into refining your models. Illustrate your experience with customer interactions, how their input can influence model adjustments, and how this ultimately improves product outcomes. Emphasizing customer-centered development emphasizes alignment with Samsara’s mission.

Craft your answer to reflect a genuine interest in Samsara’s mission to enhance safety and efficiency in physical operations. Discuss your alignment with their values, your passion for working with multimodal data, and how your skills can contribute to their innovative team. Show that you’re excited about the impact and growth opportunities this role offers.