Research Engineer, Knowledge Team
Research Engineer, Knowledge Team

Research Engineer, Knowledge Team

London Full-Time No home office possible
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At a Glance

  • Tasks: Design new information architectures and train language models for optimal data interaction.
  • Company: Join Anthropic, a mission-driven company focused on creating safe and beneficial AI systems.
  • Benefits: Enjoy competitive pay, flexible hours, generous leave, and equity donation matching.
  • Why this job: Be part of a collaborative team shaping the future of AI with real societal impact.
  • Qualifications: Strong Python skills and machine learning experience are essential; a degree in a related field is required.
  • Other info: We value diverse perspectives and encourage all candidates to apply, even if they don't meet every qualification.

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 expected salary range for this position is: Annual Salary: £250,000-£340,000 GBP.

Logistics:

  • 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 different:

We 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.

Research Engineer, Knowledge Team employer: Menlo Ventures

At Anthropic, we pride ourselves on being an exceptional employer, fostering a collaborative and innovative work culture that prioritises impactful AI research. Our team enjoys competitive compensation, generous benefits, and flexible working arrangements, all within a vibrant office environment in San Francisco. We are committed to employee growth and inclusivity, encouraging diverse perspectives to shape the future of AI technology.
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Contact Detail:

Menlo Ventures Recruiting Team

StudySmarter Expert Advice 🤫

We think this is how you could land Research Engineer, Knowledge Team

✨Tip Number 1

Familiarise yourself with Anthropic's recent research and projects. Understanding their focus areas, such as AI safety and interpretability, will help you align your discussions during interviews with their mission and values.

✨Tip Number 2

Showcase your experience with Large Language Models (LLMs) in practical scenarios. Be prepared to discuss specific projects where you've implemented or fine-tuned LLMs, as this will demonstrate your hands-on expertise relevant to the role.

✨Tip Number 3

Highlight your collaborative skills, especially in pair programming. Since the team values collaboration, sharing examples of successful teamwork and how you’ve contributed to group projects can set you apart from other candidates.

✨Tip Number 4

Prepare to discuss the societal impacts of AI and your perspective on ethical considerations. This aligns with Anthropic's mission and shows that you are not only technically skilled but also aware of the broader implications of your work.

We think you need these skills to ace Research Engineer, Knowledge Team

Advanced Python Programming
Machine Learning Research Experience
Experience with Large Language Models (LLMs)
Information Architecture Design
Reinforcement Learning Techniques
Knowledge Base Evaluation
Agentic Search Capabilities Development
Collaboration with Product Teams
Prototyping Innovative Solutions
Distributed Information Retrieval Systems
Search Engine Development
Knowledge Graphs
Query Understanding
Strong Written and Verbal Communication
Flexibility and Results Orientation
Pair Programming

Some tips for your application 🫡

Tailor Your CV: Make sure your CV highlights your experience with Python programming, machine learning, and any work you've done with Large Language Models. Use specific examples that demonstrate your skills in designing information architectures and collaborating with product teams.

Craft a Compelling Cover Letter: In your cover letter, express your passion for AI and its societal impacts. Discuss how your background aligns with Anthropic's mission and the specific responsibilities of the Research Engineer role. Be sure to mention your collaborative spirit and any relevant projects you've worked on.

Showcase Relevant Projects: If you have worked on projects related to agentic systems, knowledge graphs, or distributed information retrieval systems, include these in your application. Provide links to your GitHub or portfolio to give them a clear view of your capabilities.

Highlight Communication Skills: Since communication is highly valued at Anthropic, emphasise your written and verbal communication skills in your application. Mention any experiences where you successfully collaborated with others or presented complex ideas clearly.

How to prepare for a job interview at Menlo Ventures

✨Showcase Your Python Skills

As a Research Engineer, you'll need to demonstrate your proficiency in Python. Be prepared to discuss specific projects where you've written reliable, high-quality code. Consider sharing examples that highlight your ability to work collaboratively with teammates.

✨Discuss Your Machine Learning Experience

Make sure to articulate your machine learning research experience clearly. Talk about any projects involving Large Language Models (LLMs) and how you've applied them in real-world scenarios. This will show your understanding of the field and its applications.

✨Emphasise Collaboration and Communication

Anthropic values teamwork and communication. Be ready to discuss instances where you've successfully collaborated with product teams or other researchers. Highlight your ability to communicate complex ideas clearly, both in writing and verbally.

✨Prepare for Technical Challenges

Expect to face technical questions or challenges during the interview. Brush up on your knowledge of information architecture strategies, reinforcement learning, and agentic systems. Practising coding problems related to these topics can help you feel more confident.

Research Engineer, Knowledge Team
Menlo Ventures
Location: London
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