Research Engineer, Interpretability - job 1 of 2

As a Research Engineer in Interpretability at Anthropic, you'll be at the forefront of implementing and analyzing research experiments both in small-scale toy scenarios and large models. Your role involves establishing efficient research workflows, developing tools to support rapid experimentation, and collaborating closely with scientists to make our AI models safer. You will work on exciting projects that require optimizing distributed systems and analyzing complex data from various neural networks.

To be an ideal candidate for the Research Engineer, Interpretability position at Anthropic, you should have at least 5-10 years of experience in software engineering, deep proficiency in programming languages, especially Python, and familiarity with empirical AI research projects. Additionally, strong prioritization skills and a collaborative mindset are essential, as our team values fast-moving projects that tackle the most impactful work.

In your role as a Research Engineer, Interpretability at Anthropic, you'll engage in a variety of mind-bending projects. Examples include building tools like Garcon, which allows for easy access to LLMs' internals, or optimizing the entire pipeline for handling transformer activation data. Each project is focused on enhancing the safety and interpretability of language models, making your contributions crucial to our groundbreaking research.

At Anthropic, we prioritize diverse perspectives in all our teams, including the Research Engineer, Interpretability role. We recognize the significant social and ethical implications of AI technology. Therefore, we encourage applications from candidates of underrepresented groups and actively foster an inclusive environment where innovative ideas can flourish. We believe a diverse workforce not only creates a dynamic workplace but also enhances our research outcomes.

Career development for a Research Engineer, Interpretability at Anthropic is robust and dynamic. As a part of our team, you'll have the chance to continuously learn from collaborative research discussions, engage in impactful projects, and grow your skills. We provide an environment where you’re encouraged to push boundaries, pursue innovative research directions, and contribute to meaningful advancements in AI safety.

Mechanistic interpretability focuses on understanding how neural networks function on a fundamental level—how parameters relate to algorithms. In your response, it's great to highlight its importance in making AI systems transparent, ensuring they operate safely, and empowering developers to trust the outputs of complex models.

When asked this, reflect on a systematic approach: define clear objectives, select appropriate datasets, and determine metrics for evaluation. Explain how you'd implement your experiment while considering the scalability challenges, data management, and potentially collaborating with researchers to validate findings.

Share a specific experience detailing your contributions—whether coding experiments, optimizing frameworks, or analyzing results working alongside researchers. Emphasize your collaborative skills, ability to adapt to feedback, and how your technical expertise helped achieve the project's goals.

You can discuss your preference for Python due to libraries like PyTorch or TensorFlow, focusing on their rich ecosystems for experimentation. Make sure to mention any experiences you have with distributed computing tools or visualization platforms, backing your preferences with examples from past projects.

Discuss your strategies for staying updated, such as following key research publications, attending relevant conferences, or engaging with professional communities online. Highlight your commitment to continual learning and how you integrate new knowledge into your work, fostering innovation.

Reflect on specific challenges such as latency issues or resource allocation and explain your methodologies for overcoming them. Discuss problem-solving strategies you employed and how collaboration with colleagues provided insights or solutions that benefited the overall project.

Share a detailed story about a project that illustrates your impact, focusing on the problem you sought to solve, the role you played, the tools you used, and the successful outcomes. Highlight how this project showcased your skills and fit for the Research Engineer, Interpretability role.

Discuss the importance of fairness, accountability, and transparency in AI systems. You can mention specific industry cases to illustrate ethical dilemmas. Emphasizing your dedication to contributing to technologies that prioritize societal betterment will align well with Anthropic’s mission.

In your response, outline how you'd assess the impact and urgency of tasks. Highlight tools or frameworks you use for prioritization and discuss your experiences where effective prioritizing led to successful outcomes. Stress the importance of communication within a team to align efforts.

Convey your passion for uncovering the mysteries of neural networks and the potential to make AI systems safer and more reliable. Discuss how these interests align with Anthropic's mission and the unique contributions you hope to bring to the team.