Ground every answer in facts on this page and the original listing. We never invent Glassdoor-style reviews or salaries that are not in our data.
Expect depth on ML systems, scaling trade-offs, and research engineering judgment—plus how you ship reliable experiments. Read Anthropic’s public work on Claude and AI safety; be ready to discuss past projects that grew model quality or coverage without hand-wavy metrics.
Strong fit if you want frontier LLM research engineering at a safety-focused lab, thrive on ambiguous scaling problems, and can partner with scientists under high technical bar. Less ideal if you prefer pure product feature work or fully remote-only roles.
As a Research Engineer, Domain Scaling at Anthropic, you would likely design experiments and infrastructure that improve how Claude-scale models behave across domains—data pipelines, evaluation harnesses, training or inference scaling runs, and close work with research scientists on safety-aware capability gains. Days mix coding, experiment analysis, and write-ups that feed model iterations.
Useful foundations for this title include deep learning systems, large-scale training/eval engineering, Python, distributed compute, and careful experimental design. No certification family is specified for this listing.
Anthropic develops Claude large language models and related research with a stated focus on AI safety (Wikipedia).
It was founded in 2021 and reports roughly 2,500 employees in the provided facts.
The listing marks remote as no and location as the United States; treat it as on-site or location-bound unless Anthropic clarifies otherwise.
Website: anthropic.com
Anthropic, PBC is an American artificial intelligence (AI) company headquartered in San Francisco, California. It has developed a series of large language models (LLMs) named Claude and has a focus on AI safety. Anthropic was founded in 2021 by former members of OpenAI, including siblings Daniela Amodei and Dario Amodei, who are president and CEO, respectively. The company is privately held but plans to go public. It had an estimated valuation of $965 billion in May 2026, making it the most valuable pure-play AI company in the world.
Public cache only — not an employee review.
Anthropic is hiring a Research Engineer for our Domain Scaling team in the US. This is a full-time position based on-site at our office. About the Role You'll work within Anthropic's Domain Scaling team, contributing to our mission of building reliable, interpretable, and steerable AI systems. This role sits at the intersection of research and engineering, where you'll tackle technical challenges that enable AI models to perform effectively across diverse domains and applications. What You'll Do Design and implement experiments to evaluate how AI models generalize and scale across different problem domains Develop methods and infrastructure to test and improve model robustness in domain-specific contexts Collaborate with research scientists and engineers to translate research insights into practical systems Contribute to technical publications and share findings with the broader AI research community Participate in code reviews and engineering best practices to maintain high-quality research infrastructure Help identify and solve technical bottlenecks that arise during research experimentation What We're Looking For Strong foundation in machine learning, deep learning, or a related field Proficiency in Python and experience building machine learning systems Familiarity with modern ML frameworks and tools Ability to work effectively in a collaborative research environment Experience translating research concepts into working code Genuine interest in AI safety and developing beneficial AI systems About Anthropic Anthropic is a team of researchers, engineers, policy experts, and business leaders growing rapidly together. We're committed to ensuring AI systems are safe, beneficial, and aligned with human values. How to Apply To apply for this Research Engineer position, submit your application through this listing.
Generated for personal interview prep · 2026-07-18 UTC · getajob.ai