Research Engineer, Knowledge Team

As a Research Engineer in the Knowledge Team at Anthropic, your main responsibilities will include designing and implementing new information architecture strategies and creating advanced search capabilities for our language models. You will work on finetuning and reinforcement learning operations to enable Claude, our AI, to interact more effectively with new information architectures. Additionally, you will build knowledge base evaluation sets to identify potential failure modes, ensuring the language models work seamlessly with external data.

For the Research Engineer position at Anthropic, candidates must have at least a Bachelor's degree in a relevant field or equivalent experience. Proficiency in Python programming is essential, along with significant experience in machine learning research and working with Large Language Models like Claude. Strong communication skills and a results-oriented attitude are also important, as is the ability to collaborate effectively within a dynamic team environment.

The expected salary range for the Research Engineer position at Anthropic is between £250,000 and £340,000 GBP annually. We strive to offer competitive compensation that reflects the skills and experiences of our employees, and we also provide a comprehensive benefits package to support our team's well-being.

Yes, Anthropic does sponsor visas for the Research Engineer position. While not all roles qualify for visa sponsorship, we make every reasonable effort to assist candidates in obtaining necessary visas and work with an immigration lawyer to facilitate the process, ensuring an inclusive search for talented individuals worldwide.

At Anthropic, Research Engineers enjoy a collaborative and dynamic working environment. We encourage open communication and frequent research discussions among team members. Our work is focused on impactful AI research, allowing every engineer the opportunity to contribute to large-scale projects. We value flexibility, diversity, and a commitment to making ethical advancements in AI technology.

When discussing your Python programming experience, focus on specific projects where you created reliable and efficient code. Highlight any libraries or frameworks you used that relate to machine learning or AI development. Be sure to convey your process for producing high-quality code that enhances team collaboration.

In your response, outline significant research projects you've undertaken, especially those that involved large datasets or LLMs. Share the challenges you faced, your methodologies, and the impact your work had on the outcomes. Demonstrate how your findings contributed to advancing knowledge in the field.

Discuss your experience in cross-functional teams and how you communicate complex ideas to stakeholders with varying expertise. Emphasize your openness to feedback, willingness to share knowledge, and how you help to foster a supportive team culture. Provide examples of successful collaborative projects to illustrate your point.

Use the STAR method (Situation, Task, Action, Result) to articulate a specific instance where you encountered difficulties in model training or implementation. Highlight the strategies you adopted to identify solutions, adapt to challenges, and ultimately achieve a positive outcome, stressing your analytical skills and resilience.

Express your views on the ethical considerations surrounding AI technology, emphasizing the importance of developing responsible and interpretable models. Share how you actively incorporate these considerations into your work processes, from research phases to implementation, showcasing your commitment to creating socially beneficial AI systems.

Mention the resources you utilize to keep your knowledge current—this could include subscribing to key journals, attending conferences, participating in online courses, or engaging with professional communities. Demonstrate your proactive approach to continual learning and adapting to the fast-evolving AI landscape.

Articulate your understanding of reinforcement learning principles and how they apply to training language models. Provide examples of projects where you implemented RL techniques, discussing the results obtained and how they improved model performance. Highlight your methodological approach to tuning such models.

Describe your approach, including the steps you take from conceptualization to implementation. Discuss how you assess the requirements of different projects and the methodologies you use to test and validate your designs. Emphasize your ability to innovate and adapt solutions based on project goals.

Share specific projects where you've developed knowledge bases, including the criteria for evaluating their effectiveness. Discuss methodologies you applied to ensure robust evaluations, focusing on how this work contributed to the overall improvement of language models like Claude.

Articulate your alignment with Anthropic’s mission and values, sharing what excites you about their approach to AI research. Highlight how the role matches your skills, aspirations, and commitment to ethical AI, making a case for how you can contribute to the team and its goals moving forward.