Full-Time

Research Engineer

Societal Impacts

Confirmed live in the last 24 hours

Anthropic

Anthropic

1,001-5,000 employees

AI research and development for reliable systems

Enterprise Software
AI & Machine Learning

Compensation Overview

$315k - $340kAnnually

Mid, Senior

H1B Sponsorship Available

San Francisco, CA, USA

Only open to hiring in San Francisco for this team.

Category
Applied Machine Learning
Natural Language Processing (NLP)
AI & Machine Learning
Required Skills
Python
Data Analysis
Requirements
  • Have experience building and maintaining production-grade internal tools or research infrastructure
  • Take pride in writing clean, well-documented code in Python that others can build upon
  • Are comfortable making technical decisions with incomplete information while maintaining high engineering standards
  • Have experience with distributed systems and can design for scale and reliability
  • Have a track record of using technical infrastructure to interface effectively with machine learning models
  • Have experience deriving insights from imperfect data streams
Responsibilities
  • Design and implement scalable technical infrastructure that enables researchers to efficiently run experiments and evaluate AI systems
  • Architect systems that can handle uncertain and changing requirements while maintaining high standards of reliability
  • Lead technical design discussions to ensure our infrastructure can support both current needs and future research directions
  • Partner closely with researchers, data scientists, policy experts, and other cross-functional partners to advance Anthropic’s safety mission
  • Interface with, and improve our internal technical infrastructure and tools
  • Generate net-new insights about the potential societal impact of systems being developed by Anthropic
  • Translate insights to inform Anthropic strategy, research, and public policy

Anthropic focuses on creating reliable and interpretable AI systems. Its main product, Claude, serves as an AI assistant that can manage tasks for clients across various industries. Claude utilizes advanced techniques in natural language processing, reinforcement learning, and code generation to perform its functions effectively. What sets Anthropic apart from its competitors is its emphasis on making AI systems that are not only powerful but also understandable and controllable by users. The company's goal is to enhance operational efficiency and improve decision-making for its clients through the deployment and licensing of its AI technologies.

Company Stage

Growth Equity (Venture Capital)

Total Funding

$7.6B

Headquarters

San Francisco, California

Founded

2021

Growth & Insights
Headcount

6 month growth

73%

1 year growth

290%

2 year growth

1288%
Simplify Jobs

Simplify's Take

What believers are saying

  • The $450 million Series C financing round underscores strong investor confidence in Anthropic's growth potential.
  • The launch of Claude Pro, a subscription-based version of its generative AI model, opens new revenue streams and enhances user engagement.
  • Anthropic's collaboration with Menlo Ventures to launch the $100 million Anthology Fund positions it as a key player in accelerating AI startup innovation.

What critics are saying

  • The competitive landscape in generative AI is intensifying, with rivals like OpenAI and Cohere continuously releasing more powerful models.
  • The rapid expansion and scaling efforts, such as launching new apps and funds, may strain Anthropic's resources and operational capabilities.

What makes Anthropic unique

  • Anthropic's focus on responsible AI deployment, including measures like invisible watermarks, sets it apart in the AI landscape.
  • The launch of the $100 million Anthology Fund in collaboration with Menlo Ventures highlights Anthropic's commitment to fostering AI innovation.
  • Anthropic's multi-platform support for its Claude AI app, including vision capabilities, offers a seamless user experience across web, iOS, and Android.

Help us improve and share your feedback! Did you find this helpful?