Research Engineer: Anthropic

Role Overview
The Interpretability Team at Anthropic is dedicated to the "Mechanistic Interpretability" of AI. Rather than treating neural networks as black boxes, they aim to reverse-engineer them—analogous to neuroscience or biology—to understand how model parameters map to specific algorithms. The goal is to solve the problem of superposition, where individual neurons represent multiple unrelated concepts, making them difficult to trust.

Key Responsibilities
Experimental Implementation: Develop and analyze research experiments quickly in toy environments and at scale in production models (like Claude/Sonnet).

Workflow Optimization: Build and manage research pipelines capable of handling petabytes of transformer activations.

Tooling & Abstractions: Create internal tools (e.g., Garcon) to allow researchers to access LLM internals via Jupyter notebooks.

Visualization: Develop interactive tools to visualize complex mechanisms like attention patterns between tokens.

Qualifications & Skills
Experience: 5–10+ years of software engineering experience.

Tech Stack: Proficiency in Python is required; experience with Rust, Go, or Java is highly valued.

Domain Expertise: Familiarity with PyTorch, GPUs, Transformers, and large-scale distributed systems.

Soft Skills: Comfort with high ambiguity, a preference for fast-paced collaborative work, and a strong interest in AI ethics/safety.

Diversity & Representation
Anthropic is a Public Benefit Corporation (PBC). Their job postings explicitly encourage candidates from underrepresented groups to apply, noting that imposter syndrome often prevents qualified diverse talent from submitting applications.

While Anthropic does not frequently release granular internal demographic breakdowns, the broader AI research field (according to reports from the AI Index and various tech census data) typically sees the following representation in Research Engineering roles:

Women: ~15–20% of the AI workforce.

Black/Hispanic: ~5–8% of the AI workforce.

Anthropic actively sponsors visas for international candidates, though success depends on the specific role and candidate background.

How Anthropic Is Different
"Big Science" Approach: Unlike academic labs that work on small, specific puzzles, Anthropic works as a single cohesive team on large-scale research efforts.

Empirical View: They treat AI research as a physical science, similar to physics or biology, requiring heavy communication and collaborative discussion.