Research Scientist

As a Research Scientist at Luma, you will be primarily responsible for leading research projects in Generative AI, especially focusing on LLMs and diffusion models. Your role will include training generative models in multiple modalities, unlocking foundational models for diverse applications, and applying reinforcement learning for model tuning. You'll also be involved in data curation and the implementation of efficient training techniques on large-scale distributed systems.

To qualify for the Research Scientist role at Luma, candidates should possess a robust background in research related to Generative AI, demonstrated by leading at least one relevant project. Proficiency in deep learning frameworks such as PyTorch and Jax is essential, as well as experience with modalities like video, image, text, and audio. Familiarity with reinforcement learning and large-scale distributed systems is also beneficial.

The Research Scientist role at Luma is primarily an onsite position based in Palo Alto. However, we are open to considering remote work arrangements for candidates who possess a unique skill set that aligns with our needs. We believe in finding the right talent, regardless of their location.

At Luma, the salary for the Research Scientist position ranges from $200,000 to $300,000 per year, depending on the individual's job-related knowledge, skills, and experience. Additionally, we provide competitive equity packages in the form of stock options, along with a comprehensive benefits plan to ensure our employees feel valued and motivated.

When you apply for the Research Scientist position at Luma, your application will be reviewed by real people, not just automated systems. We strive to ensure that every applicant is given fair consideration based on their qualifications and experiences related to Generative AI research. This personal touch helps us find the best candidates who will thrive in our innovative environment.

In preparing for this question, think about a project where you made a notable contribution. Talk about the project’s goal, your specific role, the methodologies you employed, and the outcomes. Highlight any challenges you faced and how you overcame them, showcasing your problem-solving skills in the realm of Generative AI.

When answering this question, detail your experience with frameworks like PyTorch or Jax. Give specific examples of projects where you've utilized these frameworks, describing the challenges you tackled and the results achieved using deep learning techniques in Generative AI.

To address this, discuss your strategies for data curation and processing. You can mention methods you've used for cleaning data, handling biases, or ensuring diversity in the dataset, all of which are crucial for effective training in Generative AI projects.

When discussing model tuning, talk about specific techniques such as on-policy and off-policy reinforcement learning. Describe situations where you've implemented these techniques successfully and how they improved model performance, emphasizing your analytical skills.

Prepare to share a story that highlights your problem-solving ability. Describe the challenge in detail, your approach to finding a solution, and the outcome. This shows your resilience and adaptability, which are crucial for a Research Scientist role at Luma.

Share your strategies for staying informed in the fast-evolving field of Generative AI. Discuss resources like academic journals, conferences, workshops, or online courses that you engage with to continuously refresh your knowledge and skills.

Reflect on the ethical concerns that arise in the use of Generative AI, such as deepfakes, misinformation, and privacy. Providing thoughtful insights demonstrates your awareness of the broader implications associated with your work in AI research.

This question is an opportunity to express your passion for specific applications of Generative AI, such as content generation, art creation, or interactive AI. Discuss why these areas intrigue you and how you envision contributing to advancements in those fields.

Explain any experience you have working with distributed systems, highlighting relevant projects where you've had to manage data or train models across multiple nodes. This is critical for scalability and performance in modern AI applications.

Your answer should reflect genuine enthusiasm for Luma's mission and values. Mention aspects of the company or projects that align with your interests and career goals, emphasizing how you could contribute to Luma's success in the Generative AI space.