Joblaze summary

In the role of Research Engineer on the Post-Training team at Anthropic, the individual will focus on enhancing production models through advanced post-training techniques, ensuring their quality and safety. Key skills include proficiency in Python, experience with large language models, and a solid background in machine learning systems and distributed computing. This position is ideal for senior engineers who thrive in dynamic environments and can balance research with operational demands. Anthropic emphasizes collaboration and aims to create impactful AI systems, making this a unique opportunity in a rapidly evolving field.

Joblaze insights

Listed about 6 months ago — first seen on Joblaze April 14, 2026. Last confirmed on Anthropic's careers page November 3, 2026.
This exact title is also open at 1 other location at Anthropic: San Francisco, CA | New York City, NY | Seattle, WA.

Quick facts

Is the Research Engineer, Production Model Post-Training role remote?: It's hybrid — Anthropic expects some on-site time in Zürich, CH.
Where is the role based?: Anthropic is hiring for this position in Zürich, CH.
What's the tech stack?: Joblaze extracted these technologies from the posting: Constitutional AI, LLMs, Python, RLHF, deep learning frameworks, distributed computing.
Does Anthropic sponsor work visas for this role?: Yes — the posting indicates visa sponsorship is available for the right candidate.
What seniority level is this role?: Anthropic targets senior candidates for this position.
Is this full-time or contract?: Full-time for this Research Engineer, Production Model Post-Training role at Anthropic.

From the original posting

About Anthropic

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

Standard company text repeated across Anthropic's postings is omitted here.

Research Engineer, Production Model Post-Training