# Research Engineer, Pre-training

**Company:** [Anthropic](https://hotfix.jobs/companies/anthropic)
**Location:** San Francisco, CA, Seattle, WA, New York, NY
**Role:** AI Research
**Salary:** $350k – $850k/yr
**Skills:** Python, PyTorch, Transformers, Kubernetes, Gpus, Machine Learning, LLMs, Reinforcement Learning, Distributed Training, ETL
**Posted:** 2026-02-12

> Develops next-generation large language models through research, experimentation, and engineering on pre-training team. Requires strong Python/PyTorch skills, ML expertise, and MS/PhD in related field.

## Job Description

## Key Responsibilities
- Conduct research and implement solutions in model architecture, algorithms, data processing, and optimizer development
- Independently lead small research projects while collaborating on larger initiatives
- Design, run, and analyze scientific experiments to advance understanding of large language models
- Optimize and scale training infrastructure for 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 related field
- Strong software engineering skills with 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 language models
- Ability to balance research goals with practical engineering constraints
- Strong problem-solving skills and results-oriented mindset
- Excellent communication skills and collaborative work style
- Care about societal impacts of 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 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 training infrastructure
- Creating interactive visualizations of model internals

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