# Research Engineer, Production Model Post-Training at Anthropic

- Company: Anthropic (anthropic.com)
- Location: Zürich, CH
- Places: Zürich
- Posted: 2026-02-12
- Apply: https://job-boards.greenhouse.io/anthropic/jobs/5112018008
- Page: https://ye.saranatour.dev/jobs/research-engineer-production-model-post-training-anthropic-kn7ers8qd2fxvt8sbw0d0rmb4h8hdr3d

## The posting

Research Engineer, Production Model Post-Training
Anthropic · Zürich, CH

## **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**

Anthropic's production models undergo sophisticated post-training processes to enhance their capabilities, alignment, and safety. As a Research Engineer on our Post-Training team, you'll train our base models through the complete post-training stack to deliver the production Claude models that users interact with.

You'll work at the intersection of cutting-edge research and production engineering, implementing, scaling, and improving post-training techniques like Constitutional AI, RLHF, and other alignment methodologies. Your work will directly impact the quality, safety, and capabilities of our production models.

_Note: For this role, we conduct all interviews in Python. This role may require responding to incidents on short-notice, including on weekends._

## **Responsibilities:**

- Implement and optimize post-training techniques at scale on frontier models
- Conduct research to develop and optimize post-training recipes that directly improve production model quality
- Design, build, and run robust, efficient pipelines for model fine-tuning and evaluation
- Develop tools to measure and improve model performance across various dimensions
- Collaborate with research teams to translate emerging techniques into production-ready implementations
- Debug complex issues in training pipelines and model behavior
- Help establish best practices for reliable, reproducible model post-training

## **You may be a good fit if you:**

- Thrive in controlled chaos and are energised, rather than overwhelmed, when juggling multiple urgent priorities
- Adapt quickly to changing priorities
- Maintain clarity when debugging complex, time-sensitive issues
- Have strong software engineering skills with experience building complex ML systems
- Are comfortable working with large-scale distributed systems and high-performance computing
- Have experience with training, fine-tuning, or evaluating large language models
- Can balance research exploration with engineering rigor and operational reliability
- Are adept at analyzing and debugging model training processes
- Enjoy collaborating across research and engineering disciplines
- Can navigate ambiguity and make progress in fast-moving research environments

## **Strong candidates may also:**

- Have experience with LLMs
- Have a keen interest in AI safety and responsible deployment

We welcome candidates at various experience levels, with a preference for senior engineers who have hands-on experience with frontier AI systems. However, proficiency in Python, deep learning frameworks, and distributed computing is required for this role.

## **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 e