Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.
As a Research Engineer on the Clio team, you'll be responsible for maintaining and enhancing the critical infrastructure that powers Anthropic's research efforts. You'll work with systems that process data at large scales and serve many internal teams across the organization. Your work will directly enable researchers, safety teams, and other colleagues to achieve their goals by ensuring our research tools are reliable, efficient, and privacy-preserving.
In this role, you'll collaborate extensively with teams across Anthropic—from Finetuning, RL Research, and Societal Impacts to Safeguards, Communications, and customer success—to understand their diverse needs and translate them into robust technical solutions. You'll be responsible for the full lifecycle of research tools, from enhancing monitoring capabilities to improving user interfaces, all while maintaining the highest standards for data privacy and system reliability.
Responsibilities:- Enable Anthropic researchers to analyze large sets of Claude usage while preserving user privacy.
- Maintain systems that perform challenging dataset clustering and hierarchy-building.
- Debug data processing pipelines that may encounter difficult issues, such as concurrency inefficiencies or errors obscured by inter-process communications.
- Implement monitoring systems for tools that process large datasets.
- Work with internal users across teams to understand their needs and prioritize fixes and features.
- Work toward intuitive interfaces — both command-line and frontend — for research tools.
- Optimize research tools for speed and efficient resource usage.
- Enhance user data privacy protections, ensuring clear and auditable data handling practices.
- Write and maintain related documentation.
- Have 5+ years of software engineering experience with a track record of building and maintaining production systems; note that a machine learning-specific background is not critical
- Are highly proficient in Python
- Have experience with data infrastructure and large datasets in production environments
- Are comfortable working independently while maintaining collaborative relationships across teams
- Have excellent communication skills and enjoy working with diverse stakeholders to understand and solve their technical challenges
- Have experience with cloud infrastructure platforms such as AWS or GCP
- Think naturally in terms of helping move the entire organization forward, even when that means taking on work outside your primary responsibilities
- Are committed to developing AI systems responsibly and care about the societal impacts of your work
- High-performance, large-scale ML systems and distributed computing
- Kubernetes and container orchestration platforms
- GPU computing and optimization for specialized hardware
- Highly concurrent systems
- Productizing research tools and transitioning from research prototypes to production systems
- Privacy-preserving technologies and secure data handling practices
The expected salary range for this position is:
Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience.
Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.
Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.
We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.
How we're differentWe believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.
The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.
Come work with us!Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process
Top Skills
What We Do
Anthropic is an AI safety and research company that’s working to build reliable, interpretable, and steerable AI systems. Our research interests span multiple areas including natural language, human feedback, scaling laws, reinforcement learning, code generation, and interpretability.
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