Staff+ Software Engineer (Claude Science)

• Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We believe powerful AI can compress a century of scientific progress into a decade. Claude Science is a big part of building toward that future
• Claude Science is an AI workbench that gives researchers a single environment for work that today spans dozens of disconnected tools — literature, specialized databases, scientific computing, analysis, and publication-ready outputs
• Claude Science already renders protein structures, genome tracks, and chemical structures natively, and coordinates multi-agent workflows with built-in review for citation and calculation errors — and we’re just getting started. Much of this is being built 0→1 right now: you’ll shape both the product and the architecture in a category no one has defined yet
• You’ll be a technical leader who thinks holistically about the end-to-end researcher experience, partners directly with our internal research team to push model capabilities into production, and carries real ownership over what we ship next
• Our north star is accelerating scientific progress — across biology, chemistry, physics, and beyond — from early discovery through real-world application, by an order of magnitude. This work supports programs like Anthropic’s AI for Science Program and our rare disease research grants
• Ship fast against a roadmap you help shape: this is a product in a category no one has defined yet, and the highest-leverage problems are still unclaimed
• Interface directly with working scientists — academic labs, industry R&D teams, and research institutes — during key conversations, translating what you learn into engineering priorities
• Partner with product and design to turn how scientists actually work — from hypothesis to analysis to publication — into shipped product
• Work closely with research to make the models better at science: shaping evals, surfacing failure modes, and feeding what users hit in the real world back into model development

Benefits

• Comprehensive health, dental, and vision insurance for you and your dependents
• Inclusive fertility benefits via Carrot Fertility
• 22 weeks of paid parental leave
• Flexible paid time off and absence policies
• Mental health support for you and your dependents
• Competitive salary and equity packages
• Optional equity donation matching at a 1:1 ratio, up to 25% of your equity grant
• Retirement plans with competitive matching
• Life and income protection plans
• $500/month flexible wellness and time saver stipend
• Commuter benefits
• Annual education stipend
• Home office stipends
• Relocation support for those moving for Anthropic
• Daily meals and snacks in the office- Are comfortable working directly with technical domain experts and translating what you learn
• Have built products from 0 to 1 in fast-moving environments, and can set technical direction with limited precedent to lean on
• Have built AI products and know what it takes to turn model capabilities into applications people actually use
• Drive cross-team alignment to ship impactful work, with influence over authority
• Have 8+ years of software engineering experience, ideally with 2+ years at a Staff or equivalent technical leadership level
• 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
• We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you’re interested in this work
• Background in chemistry, biology, physics, or another science
• Experience working with research teams to improve domain-specific model capabilities, including evaluation frameworks