Research Engineer (Production Model Post Training)

Anthropic Limited
San Francisco, CA, United States
1 day ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Compensation
$315,000.0
Working hours
Regular working hours
Job source

Tech stack

Artificial Intelligence Software Debugging Distributed Systems Python (Programming Language) Software Safety Software Engineering High Performance Computing Large Language Models Deep Learning Machine Learning Operations Claude

Job description

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 develop and optimize the systems that transform our base models into the refined 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., * Implement and optimize post-training techniques at scale on frontier models

  • 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

Requirements

  • 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
  • Have a keen interest in AI safety and responsible deployment
  • Experience with LLMs is a significant plus
  • Proficiency in Python, deep learning frameworks, and distributed computing is required for this role

We welcome candidates at various experience levels, with a preference for senior engineers who have hands-on experience with frontier AI systems., Education requirements: We require at least a Bachelor’s degree in a related field or equivalent experience.

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.

Benefits & conditions

Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. #J-18808-Ljbffr

About the company

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 believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact - advancing our long-term goals of steerable, trustworthy AI - rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We’re an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.

The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.

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