Staff Applied Scientist (Recommender Systems)

As a Staff Applied Scientist at Robin AI, your key responsibilities include leading research to enhance recommender systems within the legal AI domain, developing algorithms for interpretability and personalization, and conducting experiments to refine language models. You'll also mentor junior researchers and stay updated with the latest advancements in AI and machine learning.

To qualify for the Staff Applied Scientist position at Robin AI, candidates should ideally hold a Ph.D. in Computer Science, Data Science, or a related field. A strong background in machine learning, experience with recommender systems, and skills in statistical methods and optimization techniques are essential for success in this role.

The Staff Applied Scientist at Robin AI plays a crucial role in fostering a collaborative work environment by mentoring junior researchers, encouraging knowledge sharing, and promoting innovation within interdisciplinary teams. This interaction not only enhances the team's capabilities but also cultivates a positive workplace culture.

Staff Applied Scientists at Robin AI are provided with ample growth opportunities, including promotions for high performers and career advancement support. The company's commitment to equity also ensures that all employees are considered for ownership and leadership roles.

Working as a Staff Applied Scientist at Robin AI comes with a competitive salary and an attractive equity package, along with a flexible, hybrid work schedule. Additionally, benefits include comprehensive health coverage and substantial annual leave, ensuring a healthy work-life balance.

When answering this question, detail your past projects involving recommender systems, focusing on algorithms used, challenges faced, and how you measured effectiveness. Highlight any specific results or insights gained from these systems to demonstrate your impact.

Discuss techniques like model interpretability methods, including LIME or SHAP, and how these can be used to clarify decision-making processes for users. Share your experiences implementing these techniques in prior work to showcase your expertise.

Explain your mentoring philosophy and approach, including how you provide guidance, encourage independent thinking, and foster a sense of collaboration. Share specific instances where your mentorship led to improved team performance or innovation.

Detail your experience, including frameworks you’ve worked with and specific projects that involved large language models. Talk about any challenges faced in this area and how you addressed them.

Discuss your strategies for staying updated, such as attending conferences, participating in online courses, following key publications, or engaging with professional networks. Mention specific sources that are invaluable to you.

Choose a specific problem to describe, detailing the approach you took, the methodologies used, and the outcomes. Highlight your role in the project and what you learned from the experience.

Share an experience where an experiment didn't go as planned and explain how you handled it. Discuss the lessons learned and how you applied this knowledge to future projects, showcasing resilience and a growth mindset.

Elaborate on any specific projects involving reinforcement learning, discussing the algorithms used and the outcomes achieved. Provide insight into how reinforcement learning can be applied within recommender systems.

Share a specific example where you were able to enhance a model's performance, describing the steps taken, data utilized, and results achieved. This will demonstrate your analytical skills and results-oriented mindset.

Discuss your methods for collecting and analyzing user feedback, and how this feedback informs your adjustment of models or algorithms. Highlight examples of user-driven improvements in past projects.