Research Engineer, LLM

As a Research Engineer focusing on Large Language Models at Anyscale, you'll primarily collaborate with teams to develop features like the Accelerated DAG API and enhance Ray for large-scale training. Your role involves both intensive coding—approximately 70% of your time—and research activities to ensure we remain at the forefront of machine learning systems.

To apply for the Research Engineer position at Anyscale, you should possess a graduate degree (MSc or PhD) in machine learning, NLP, CV, or ML systems. You should also have production-level experience in relevant areas, ideally with a minimum of 4 years of professional work and a published paper in a top-tier AI conference.

As a Research Engineer working with LLMs at Anyscale, it is crucial to have strong programming skills, particularly in Python and Pytorch. These languages are foundational for developing scalable machine learning applications and engaging with distributed ML systems within our innovative framework.

At Anyscale, the team dynamic for a Research Engineer is highly collaborative. You'll work closely with the Ray Core and Ray Train teams, sharing insights and expertise to bridge gaps between research and engineering, ensuring that we deliver the best possible product to our users.

The target salary for a Research Engineer, LLM at Anyscale ranges from $170,112 to $237,000, depending on experience. Additionally, you will enjoy various benefits such as stock options, a comprehensive healthcare plan, 401k, paid parental leave, and generous flexible time off policies to maintain a healthy work-life balance.

To effectively answer this question during your interview, focus on specific projects where you've applied machine learning systems. Mention tools, frameworks like Ray, and programming languages such as Python and Pytorch. Highlight your contributions and the impact of your work on previous teams or projects.

You can approach this question by discussing how the Accelerated DAG API can enhance performance and scalability in ML workflows. Talk about your knowledge of Directed Acyclic Graphs and how they help represent computations, particularly in tracking and optimizing dependencies in large-scale ML applications.

Prepare for this question by sharing a particular challenge that showcases your analytical problem-solving skills. Discuss the nature of the challenge, the approach you took to resolve it, and the ultimate positive outcome, emphasizing teamwork and technical skills.

Highlight your strategies for continuous learning and professional development, such as attending conferences, following key researchers in your fields, and engaging with online courses or forums. Be specific about your favorite sources of information and any particular research that excites you.

Discuss your familiarity with various benchmarking frameworks and metrics for evaluating ML systems. Provide insight into how you approach optimization challenges, focusing on performance improvements and scalability, along with any tools you typically use.

When addressing this question, outline your past research experiences, the focus of your published work, and how it contributed to the field or your understanding of ML. Be prepared to discuss the research process, peer review experience, and any lessons learned.

In your response, you might want to outline Ray's unique features, scalability, and ease of integration with other tools. Share any experiences you've had with Ray, including performance metrics or adaptations you've made to enhance your work with distributed learning.

This is your chance to shine a light on your collaborative spirit. Describe the project context, your role, and how you successfully navigated teamwork dynamics. Highlight clear communication and adaptability as key factors for project success.

Discuss the critical relationship between research and practical applications. Emphasize how research lays the groundwork for new advancements and innovations while also contributing to industry best practices and the evolution of existing algorithms.

Share your learning strategies such as stepping through documentation, experimenting with applications, seeking mentorship from colleagues, or attending workshops. Highlight your commitment to staying relevant in a fast-paced field like ML and LLM.