# Research Engineer/Research Scientist, Pre-training

**Company**: Anthropic
**Location**: San Francisco, CA
**Work arrangement**: hybrid
**Experience**: senior
**Job type**: full-time
**Salary**: $350,000-$850,000 USD
**Category**: Engineering
**Industry**: Technology
**Wikidata**: https://www.wikidata.org/wiki/Q116758847

**Apply**: https://job-boards.greenhouse.io/anthropic/jobs/4616971008?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply
**Canonical**: https://yubhub.co/jobs/job_829eb6af-7db

## Description

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.

We're seeking a Research Engineer to join our Pre-training team, responsible for developing the next generation of large language models. In this role, you will work at the intersection of cutting-edge research and practical engineering, contributing to the development of safe, steerable, and trustworthy AI systems.

**Key Responsibilities:**

- Conduct research and implement solutions in areas such as model architecture, algorithms, data processing, and optimizer development

- Independently lead small research projects while collaborating with team members on larger initiatives

- Design, run, and analyze scientific experiments to advance our understanding of large language models

- Optimize and scale our training infrastructure to improve efficiency and reliability

- Develop and improve dev tooling to enhance team productivity

- Contribute to the entire stack, from low-level optimizations to high-level model design

**Qualifications:**

- Advanced degree (MS or PhD) in Computer Science, Machine Learning, or a related field

- Strong software engineering skills with a proven track record of building complex systems

- Expertise in Python and experience with deep learning frameworks (PyTorch preferred)

- Familiarity with large-scale machine learning, particularly in the context of language models

- Ability to balance research goals with practical engineering constraints

- Strong problem-solving skills and a results-oriented mindset

- Excellent communication skills and ability to work in a collaborative environment

- Care about the societal impacts of your work

**Preferred Experience:**

- Work on high-performance, large-scale ML systems

- Familiarity with GPUs, Kubernetes, and OS internals

- Experience with language modeling using transformer architectures

- Knowledge of reinforcement learning techniques

- Background in large-scale ETL processes

**Sample Projects:**

- Optimizing the throughput of novel attention mechanisms

- Comparing compute efficiency of different Transformer variants

- Preparing large-scale datasets for efficient model consumption

- Scaling distributed training jobs to thousands of GPUs

- Designing fault tolerance strategies for our training infrastructure

- Creating interactive visualizations of model internals, such as attention patterns

**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!

**Compensation:**

- Annual Salary: $350,000-$850,000 USD

## Skills

### Required
- Python
- deep learning frameworks
- large-scale machine learning
- model architecture
- algorithms
- data processing
- optimizer development

### Nice to have
- GPUs
- Kubernetes
- OS internals
- language modeling
- transformer architectures
- reinforcement learning techniques
- large-scale ETL processes

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Source: [Apply at job-boards.greenhouse.io](https://job-boards.greenhouse.io/anthropic/jobs/4616971008?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)