Data Scientist (LLM)

As a Data Scientist specializing in Large Language Models at Veritone, your primary responsibilities will include researching, developing, and deploying LLM-based applications. You will work on fine-tuning pre-trained LLMs for specific tasks, evaluate different models to determine the best fit for various use cases, and collaborate with cross-functional teams to identify opportunities for integration. Additionally, maintaining data pipelines and monitoring model performance will be key parts of your role.

To become a Data Scientist focusing on large Language Models at Veritone, you will need a Master's degree in Computer Science, Data Science, or a related field. A strong background in machine learning and deep learning is crucial, along with experience in working with and fine-tuning LLMs like GPT or BERT. Proficiency in Python and knowledge of relevant libraries, as well as excellent problem-solving skills, are essential qualifications for this role.

For the Data Scientist position focusing on LLMs at Veritone, familiarity with Python is a must. You should also be comfortable working with machine learning libraries such as TensorFlow, PyTorch, Hugging Face, and Transformers, which are essential for building and optimizing LLMs. Understanding of data preprocessing techniques for handling text data will also be beneficial for the role.

At Veritone, we value continuous learning and personal growth. As a Data Scientist focusing on LLMs, you will be encouraged to stay updated with the latest research and advancements in the field. We provide resources for ongoing education and professional development, ensuring that you have opportunities for training, workshops, and conferences to enhance your skills and knowledge in AI and LLM technologies.

The work environment for a Data Scientist at Veritone is dynamic and collaborative. We offer a hybrid remote work model that empowers you to work in a way that suits your lifestyle, fostering a culture of flexibility and innovation. You'll engage with cross-functional teams and contribute to exciting projects that utilize cutting-edge LLM technology, all while working in a fast-paced and supportive atmosphere.

When answering this question, begin by detailing specific Large Language Models you’ve worked with, such as GPT or BERT. Discuss particular projects where you've implemented these models, including the challenges you faced and how you overcame them. Be sure to highlight the results and insights gained from your work, demonstrating your understanding of LLMs and their impact on business outcomes.

To effectively answer this question, you can mention common evaluation metrics such as accuracy, F1 score, or perplexity. Describe the approaches you use for benchmarking different models and how you decide which model is best suited for a specific application. Providing examples from your experience will add credibility to your response.

Explain your process for fine-tuning in detail. Start by discussing the importance of task-specific datasets, followed by how you adapt the architecture and hyperparameters of the model to fit your goals. You can also note the techniques you use for avoiding overfitting during this process, highlighting your analytical thinking and problem-solving skills.

Share a specific scenario that emphasizes your problem-solving abilities. Describe the nature of the challenge, your approach to addressing it, and the lessons learned from the experience. Make sure to illustrate how this experience contributes to your overall capabilities as a Data Scientist focused on LLMs.

Discuss the tools you’re proficient in, such as TensorFlow, PyTorch, or Hugging Face. Highlight your experience with cloud platforms if applicable, and mention any deployment practices or tools you’ve used, such as Docker or Kubernetes. Emphasize how these tools have added efficiency in your workflow.

Emphasize your commitment to continuous learning by mentioning sources like academic journals, conferences, online courses, and professional networks. Discuss how you apply this knowledge to your projects, showcasing an eagerness to innovate and advance within the field of AI.

In your response, detail specific NLP techniques you’ve worked with, such as tokenization, named entity recognition, or sentiment analysis. Provide examples of projects where you applied these techniques, focusing on the outcomes and how they contributed to your overall success as a Data Scientist.

Explain your communication style and how you ensure that everyone on the team is aligned toward common goals. Discuss any tools or frameworks you use to facilitate collaboration, which showcases your adaptability and ability to work in a team-oriented environment.

Share your positive attitude toward receiving constructive criticism. Discuss how you view feedback as an opportunity to grow and improve, emphasizing your openness to mentorship and collaboration. Provide examples of how you’ve implemented feedback on past projects to produce better results.

Convey your passion for AI by discussing the potential it has to transform industries and improve lives. For LLMs, mention the advancements in natural language understanding they provide and how they open avenues for more human-like interactions between machines and users. Highlight your enthusiasm for being at the cutting edge of technology and the contributions you aspire to make.