Research Engineer, AI for Chip Design
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Looking for a research engineer job in San Francisco? Choose from 79 open roles at 23 employers. Explore real salary data and skills breakdown across remote, hybrid and on-site roles.
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The titles and summaries suggest work that sits at the intersection of rigorous engineering and active research. Several roles at Anthropic centre on building and refining reinforcement learning environments for specific domains: chip design asks for deep ASIC or FPGA knowledge, while code RL focuses on training frontier models to write and debug real software end-to-end. Computer Use combines computer vision with agentic capability work, and Domain Scaling involves creating RL environments across finance, healthcare, and legal verticals. Life Sciences pairs ML engineering with scientific rigour around biology. Across almost all sample summaries, designing evaluation frameworks is a core responsibility, reflected in the broader skills counts. Model Evaluations makes this explicit, requiring candidates to build distributed infrastructure that runs evaluations reliably at scale and own monitoring during training runs. Other titles—Interpretability, Safeguards, Cybersecurity RL, Economic Research Data Platform—point to a wide spread of application areas beyond the sample summaries provided.
The skills counts reflect mentions in posting descriptions across all postings on this page, not formal requirements. Evaluation appears most widely, followed by LLMs and reinforcement learning, with Python and PyTorch also prominent. Lower-frequency mentions—CUDA, Go, A/B testing, publications—appear in a meaningful minority. Because the counts come from a single snapshot of open roles, they indicate current emphasis rather than stable industry norms.
The employers' own advertised ranges, for individual roles at different levels — not an average and not a market rate.
On-site is by far the predominant arrangement in these postings; only a small minority state hybrid working, and fully remote roles are rare. The two hybrid sample summaries from Anthropic specify at least 25 per cent office time in San Francisco, so even hybrid here implies regular physical presence. No postings in the set have an unknown work mode, which is unusually complete, but candidates should still confirm exact attendance expectations directly with each employer, as stated policies can differ from day-to-day practice.
Jobs checked 1 hour ago. · Guide reviewed 14 September 2026.
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