Research Engineer, Knowledge Team
Anthropic
| Company | Anthropic |
| Category | Engineering |
| Location | Remote-Friendly (Travel-Required) | San Francisco |
| Remote | Remote |
| Employment | Not stated |
| Level | Not stated |
| Salary | Not stated by the employer |
| Posted | 23 Apr 2024 |
| Last verified | 30 Jul 2026 |
| Source | Employer career page (greenhouse) |
Description
About Anthropic
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.
About the role:
We are looking for Research Engineers to help us redesign how Claude interacts with external data sources. Many of the paradigms for how data and knowledge bases are organized assume human consumers and constraints. This is no longer true in a world of LLMs! Your job will be to design new architectures for how information is organized, and train language models to optimally use those architectures.
Responsibilities:
Designing and implementing from scratch new information architecture strategies
Performing finetuning and reinforcement learning to teach language models how to interact with new information architectures
Building “hard” knowledge base eval sets to help identify failure modes of how language models work with external data
Designing and evaluating advanced agentic search capabilities.
You may be a good fit if you:
Are a very experienced Python programmer who can quickly produce reliable, high quality code that your teammates love using
Have good machine learning research experience
Have experience developing software that utilizes Large Language Models such as Claude
Are results-oriented, with a bias towards flexibility and impact
Pick up slack, even if it goes outside your job description
Enjoy pair programming (we love to pair!)
Want to partner with world-class ML researchers to develop new LLM capabilities
Care about the societal impacts of your work
Have clear written and verbal communication
Strong candidates will also have experience with:
Collaborating with product teams to quickly prototype and deliver innovative solutions
Building complex agentic systems that utilize LLMs
Developing scalable distributed information retrieval systems, such as search engines, knowledge graphs, RAG, indexing, ranking, query understanding, and distributed data processing
The annual compensation range for this role is listed below.
For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $350,000 — $850,000 USD Logistics
Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience
Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience
Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position
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 repres
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